Neuropsychologia 41 (2003) 1942--1958

Numerical abilities are thought to rest on the integration of two distinct systems, a verbal system of number words and a non-symbolic representation of approximate quantities. This view has lead to the classification of acalculias into two broad categories depending on whether the deficit affects the verbal or the quantity system. Here, we test the association of deficits predicted by this theory, and particularly the presence or absence of impairments in non-symbolic quantity processing. We describe two acalculic patients, one with a focal lesion of the left parietal lobe and Gerstmann's syndrome and another with semantic dementia with predominantly left temporal hypometabolism. As predicted by a quantity deficit, the first patient was more impaired in subtraction than in multiplication, showed a severe slowness in approximation, and exhibited associated impairments in subitizing and numerical comparison tasks, both with Arabic digits and with arrays of dots. As predicted by a verbal deficit, the second patient was more impaired in multiplication than in subtraction, had intact approximation abilities, and showed preserved processing of non-symbolic numerosities

The SCREECH OWL reasoner

We present a preliminary version of the approximate OWL reasoning system SCREECH. It builds on the KAON2 system and performs OWL ABox reasoning in an approximate manner. It trades soundness of reasoning for efficiency, with resulting polynomial worst-case data complexity. It has been developed for use in time-critical applications where quick response time is more important than a full guarantee of correctness of answers. The theoretical background for the system is explained in [Hitzler and Vrande ci c, 2005] and is being presented at the conference

Reduced Dimension Approach

Exact or approximate solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear state feedback defined on a polyhedral partition of the state space. However, the complexity of the polyhedral partition often increases rapidly with the dimension of the state vector, and the number of constraints. This paper presents an approach for reducing the dimension of the approximate explicit solution to linear constraint MPC problems

On Tuning The (, )-Sequential-Sampling Algorithm For

We present a very efficient variant of the (#, #)- SEQUENTIAL-SAMPLING algorithm, recently introduced by the authors, for the #-approximate string matching problem with #-bounded gaps, which often arises in many questions on musical information retrieval and musical analysis