The TSIMMIS Approach to Mediation: Data Models and Languages

TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.     

Querying websites using compact skeletons

Several commercial applications, such as online comparison shopping and process automation, require integrating information that is scattered across multiple websites or XML documents. Much research has been devoted to this problem, resulting in several research prototypes and commercial implementations. Such systems rely on wrappers that provide relational or other structured interfaces to websites. Traditionally, wrappers have been constructed by hand on a per-website basis, constraining the scalability of the system. We introduce a website structure inference mechanism called compact skeletons that is a step in the direction of automated wrapper generation. Compact skeletons provide a transformation from websites or other hierarchical data, such as XML documents, to relational tables. We study several classes of compact skeletons and provide polynomial-time algorithms and heuristics for automated construction of compact skeletons from websites. Experimental results show that our heuristics work well in practice. We also argue that compact skeletons are a natural extension of commercially deployed techniques for wrapper construction.     

Generalization to Novel Views: Universal,Class-based,and Model-based Processing

A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewing conditions such as viewing position and illumination. In this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place: universal, class, and model-based. The first is applicable equally to all objects, the second to a class of objects, and the third uses known properties of individual objects. We derive theoretical limitations on each of the three generalization levels. For the universal level, previous results have shown that no invariance can be obtained. Here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed. We also extend the results to changes of illumination direction. For the class level, previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist. Here, we distinguish between classes that admit such invariance and classes that do not. We demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects. Furthermore, we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level, when the objects are restricted to belong to a given class, an invariant function to illumination direction can be defined. A general conclusion of this study is that class-based processing, that has not been used extensively in the past, is often advantageous for dealing with variations due to viewpoint and illuminant changes.     

Can imageability help us draw the line between storage and composition?

Language requires both storage and composition. However, exactly what is retrieved from memory and what is assembled remains controversial, especially for inflected words. Here, “imageability effects” is introduced as a new diagnostic of storage and a complement to frequency effects. In 2 studies of past-tense morphology, more reliable imageability and frequency effects were found on irregulars than on regulars. An interaction with sex was also observed: Males but not females showed more reliable frequency and imageability effects for irregulars than for regulars; females but not males showed signs of storage for regulars, particularly among higher frequency forms. Overall, the findings validate imageability effects as another diagnostic of storage and suggest that the line between storage and computation is not a simple function; rather, it depends upon the interplay of both item- and subject-specific factors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What to do next: Using problem status to determine the course of action

Formal decision support tools are little used in engineering design. This paper explores the reasons for this and presents a method which is tailored to problems characterized by teams of stakeholders with inconsistent views who generate multiple alternatives and criteria, and who work to reach consensus. This method is especially designed to support activity when much of the information is qualitative, immature, and there is a diversity of views. The methodology assists the team in determining which alternative attributes to invest time in refining in their effort to reach consensus. The underlying mathematical structure (a Bayesian model of multi-attribute team decision making) is presented. This model supports team member belief about an alternative's ability to meet a criteria on two dimensions, knowledge and confidence. The methodology forces recording the rationale used to reach the final decision. A running example is used to explain the details.This research has been supported by the National Science Foundation under grant DDM-931996. The opinions in this paper are the authors' and do not reflect the position of the NSF or Oregon State University.     

Three-dimensional object recognition based on the combination of views

Visual object recognition is complicated by the fact that the same 3D object can give rise to a large variety of projected images that depend on the viewing conditions, such as viewing direction, distance, and illumination. This paper describes a computational approach that uses combinations of a small number of object views to deal with the effects of viewing direction. The first part of the paper is an overview of the approach based on previous work. It is then shown that, in agreement with psychophysical evidence, the view-combination approach can use views of different class members rather than multiple views of a single object, to obtain class-based generalization. A number of extensions to the basic scheme are considered, including the use of non-linear combinations, using 3D versus 2D information, and the role of coarse classification on the way to precise identification. Finally, psychophysical and biological aspects of the view-combination approach are discussed. Compared with approaches that treat object recognition as a symbolic high-level activity, in the view-combination approach the emphasis is on processes that are simpler and pictorial in nature.     

Monte Carlo simulation of a mammographic test phantom

A test phantom, including a wide range of mammographic tissue equivalent materials and test details, was imaged on a digital mammographic system. In order to quantify the effect of scatter on the contrast obtained for the test details, calculations of the scatter-to-primary ratio (S/P) have been made using a Monte Carlo simulation of the digital mammographic imaging chain, grid and test phantom. The results show that the S/P values corresponding to the imaging conditions used were in the range 0.084-0.126. Calculated and measured pixel values in different regions of the image were compared as a validation of the model and showed excellent agreement. The results indicate the potential of Monte Carlo methods in the image quality-patient dose process optimisation, especially in the assessment of imaging conditions not available on standard mammographic units.