This paper extends the work of Gottlob, Lee, and Valiant (PODS 2009) [9], and considers worst-case bounds for the size of the result Q(D) of a conjunctive query Q to a database D given an arbitrary set of functional dependencies. The bounds in [9] are based on a “coloring” of the query variables. In order to extend the previous bounds to the setting of arbitrary functional dependencies, we leve...

Model checking based on the causal partial order semantics of Petri nets is an approach widely applied to cope with the state space explosion problem. One of the ways to exploit such a semantics is to consider (finite prefixes of) net unfoldings — themselves a class of acyclic Petri nets — which contain enough information, albeit implicit, to reason about the reachable markings of the original ...

In this work, we propose a new framework for learning mixture models from continuous data. Gaussian Mixture Models (GMMs) are commonly used for this task and are popular among practitioners because of their sound statistical foundation and the availability of an efficient learning algorithm [2]. However, the underlying assumption about the normally distributed mixing components, is often too ri...

We study inference systems of weak functional dependencies in relational and complex-value databases. Functional dependencies form a very common class of database constraints. Designers and administrators proficiently utilise them in everyday database practice. Functional dependencies correspond to the linear-time decidable fragment of Horn clauses in propositional logic. Weak functional depend...

In this paper we study both the value function and Q-function formulation of the Linear Programming (LP) approach to ADP. The approach selects from a restricted function space to fit an approximate solution to the true optimal Value function and Q-function. Working in the discrete-time, continuous-space setting, we extend and prove guarantees for the fitting error and online performance of the ...

We present a novel linear program for the approximation of the dynamic programming costto-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural ‘projection’ of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go fun...

This paper presents a variable bit rate ADP-CELP (Adaptive Density Pulse Code Excited Linear Prediction) coder that selects one of four kinds of coding structure in each frame based on short time speech characteristics. To improve speech quality and reduce the average bit rate, we have developed a speech/non-speech classification method using spectrum envelope variation, which is robust for bac...

In two replication studies we examined response bias and dependencies in voluntary decisions. We trained a linear classifier to predict "spontaneous decisions" and in the second study "hidden intentions" from responses in preceding trials and achieved comparable prediction accuracies as reported for multivariate pattern classification based on voxel activities in frontopolar cortex. We discuss ...

This paper exposes the research being done about the incorporation of copula functions in supervised classification. It is shown, by means of pixel classification, the advantages that modeling dependencies provides to supervised classification and the benefits of doing it through copula functions which are not limited to linear dependencies. The experiments executed so far, show positive result...