Fully Adaptive Gaussian Mixture Metropolis-Hastings Algorithm

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multi-modal and multi-dimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided

Backward chaining inference as a database stored procedure – the experiments on real-world knowledge bases

In this work, two approaches of backward chaining inference implementation were compared. The first approach uses a classical, goal-driven inference running on the client device – the algorithm implemented within the KBExpertLib library was used. Inference was performed on a rule base buffered in memory structures. The second approach involves implementing inference as a stored procedure, run in the environment of the database server – an original, previously not published algorithm was introduced. Experiments were conducted on real-world knowledge bases with a relatively large number of rules. Experiments were prepared so that one could evaluate the pessimistic complexity of the inference algorithm. This work also includes a detailed description of the classical backward inference algorithm – the outline of the algorithm is presented as a block diagram and in the form of pseudo-code. Moreover, a recursive version of backward chaining is discussed

Type-Based Detection of XML Query-Update Independence

This paper presents a novel static analysis technique to detect XML query-update independence, in the presence of a schema. Rather than types, our system infers chains of types. Each chain represents a path that can be traversed on a valid document during query/update evaluation. The resulting independence analysis is precise, although it raises a challenging issue: recursive schemas may lead to infer infinitely many chains. A sound and complete approximation technique ensuring a finite analysis in any case is presented, together with an efficient implementation performing the chain-based analysis in polynomial space and time.Comment: VLDB201

A framework for deadlock detection in core ABS

We present a framework for statically detecting deadlocks in a concurrent object-oriented language with asynchronous method calls and cooperative scheduling of method activations. Since this language features recursion and dynamic resource creation, deadlock detection is extremely complex and state-of-the-art solutions either give imprecise answers or do not scale. In order to augment precision and scalability we propose a modular framework that allows several techniques to be combined. The basic component of the framework is a front-end inference algorithm that extracts abstract behavioural descriptions of methods, called contracts, which retain resource dependency information. This component is integrated with a number of possible different back-ends that analyse contracts and derive deadlock information. As a proof-of-concept, we discuss two such back-ends: (i) an evaluator that computes a fixpoint semantics and (ii) an evaluator using abstract model checking.Comment: Software and Systems Modeling, Springer Verlag, 201