Map interpretation remains a critical step in solving the structure of a macromolecule. Errors introduced at this early stage may persist throughout crystallo-graphic refinement and result in an incorrect struc-ture. The normally quoted crystallographic residual is often a poor description

. However, training with a maximum likelihood score will produce non-sparse kernel machines. Instead, we train an SVM, then train the parameters of an additional sigmoid function to map the SVM outputs into probabilities. This chapter compares classification error rate and likelihood scores for an SVM plus

software we will gain access to more data, but not improve access to the type and quality of information needed for decision making. To develop the concepts needed for future information systems we model information processing as an interaction of data and knowledge. This model provides criteria for a high

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor

mappings between concepts in different terminology systems is also possible with SKOS mapping properties. This poster discusses potential quality issues in using SKOS to express terminology mappings. Problematic patterns are defined and corresponding rules are developed to automatically detect situations

of uncovering relationships between sensing capabilities and map quality. We describe a sensor model which encapsulates most of the interesting aspects of range-bearing sensors used for mapping, including sensing radius, beam width, range and bearing noise, and so on. We then apply the model to occupancy grid

the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data

of these algorithms, a very broad and flexible framework is investigated . Several crucial new concepts which allow a systematic discussion of questions on behaviour in general Hilbert spaces and on the quality of convergence are brought out. Numerous examples are given. 1991 M.R. Subject Classification. Primary 47H

data sources. Spicy brings together schema matching and mapping generation tools to further automate this process. A key observation, here, is that the quality of the mappings is strongly influenced by the quality of the in-put correspondences. To address this problem, Spicy adopts a three

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