The Wall Street Journal experiment (and useful programs)

This document gives information on parsing experiments applied to the standard Wall Street Journal corpus (``Standard'' means that this corpus has been widely used for exhibiting parsing tests of various models). The tested syntactic models are : standard Stochastic Context-Free Grammars, standard Tree Substitution Grammars, Gibbsian Context-Free Grammars and Gibbsian Tree Substitution Grammars. The parsing experiments are described with deep details so as to enable reader to easily redo the experiments from scratch (i.e. preparing the database, training and evaluating the models). The programs developped for these experiments are also described

Probabilistic Image Models and their Massively Parallel Architectures : A Seamless Simulation- and VLSI Design-Framework Approach

Algorithmic robustness in real-world scenarios and real-time processing capabilities are the two essential and at the same time contradictory requirements modern image-processing systems have to fulfill to go significantly beyond state-of-the-art systems. Without suitable image processing and analysis systems at hand, which comply with the before mentioned contradictory requirements, solutions and devices for the application scenarios of the next generation will not become reality. This issue would eventually lead to a serious restraint of innovation for various branches of industry. This thesis presents a coherent approach to the above mentioned problem. The thesis at first describes a massively parallel architecture template and secondly a seamless simulation- and semiconductor-technology-independent design framework for a class of probabilistic image models, which are formulated on a regular Markovian processing grid. The architecture template is composed of different building blocks, which are rigorously derived from Markov Random Field theory with respect to the constraints of \it massively parallel processing \rm and \it technology independence\rm. This systematic derivation procedure leads to many benefits: it decouples the architecture characteristics from constraints of one specific semiconductor technology; it guarantees that the derived massively parallel architecture is in conformity with theory; and it finally guarantees that the derived architecture will be suitable for VLSI implementations. The simulation-framework addresses the unique hardware-relevant simulation needs of MRF based processing architectures. Furthermore the framework ensures a qualified representation for simulation of the image models and their massively parallel architectures by means of their specific simulation modules. This allows for systematic studies with respect to the combination of numerical, architectural, timing and massively parallel processing constraints to disclose novel insights into MRF models and their hardware architectures. The design-framework rests upon a graph theoretical approach, which offers unique capabilities to fulfill the VLSI demands of massively parallel MRF architectures: the semiconductor technology independence guarantees a technology uncommitted architecture for several design steps without restricting the design space too early; the design entry by means of behavioral descriptions allows for a functional representation without determining the architecture at the outset; and the topology-synthesis simplifies and separates the data- and control-path synthesis. Detailed results discussed in the particular chapters together with several additional results collected in the appendix will further substantiate the claims made in this thesis

Morris Catalog 2001-03

This catalog is the second semester-based catalog produced for the University of Minnesota, Morris. It covers academic years 2001-2002 and 2002-2003. The Morris Catalog is in effect for nine years; this catalog is in effect from fall 2001 through the end of summer session 2010.https://digitalcommons.morris.umn.edu/catalog/1005/thumbnail.jp

Morris Catalog 2003-05

This catalog is the third semester-based catalog produced for the University of Minnesota, Morris. It covers academic years 2003-2004 and 2004-2005. The Morris Catalog is in effect for nine years; this catalog is in effect from fall 2003 through the end of summer session 2012.https://digitalcommons.morris.umn.edu/catalog/1006/thumbnail.jp

Morris Catalog, 2015-17

https://digitalcommons.morris.umn.edu/catalog/1001/thumbnail.jp

Morris Catalog 2011-13

This catalog covers academic years 2011–12 and 2012–13.https://digitalcommons.morris.umn.edu/catalog/1010/thumbnail.jp

Morris Catalog 2017-19

This catalog covers academic years 2017–18 and 2018–19. The Morris Catalog is in effect for nine years; this catalog is in effect from fall 2017 through the end of summer session 2026.https://digitalcommons.morris.umn.edu/catalog/1002/thumbnail.jp

Morris Catalog 2019-21

This catalog covers academic years 2019–20 and 2020–21. The Morris Catalog is in effect for nine years; this catalog is in effect from fall 2019 through the end of summer session 2028.https://digitalcommons.morris.umn.edu/catalog/1003/thumbnail.jp

Morris Catalog 2013-15

https://digitalcommons.morris.umn.edu/catalog/1000/thumbnail.jp

Random Graph Modeling and Discovery

In the first part of this thesis, we present a general class of models for random graphs that is applicable to a broad range of problems, including those in which graphs have complicated edge structures. These models need not be conditioned on a fixed number of vertices, as is often the case in the literature, and can be used for problems in which graphs have attributes associated with their vertices and edges. To allow structure in these models, a framework analogous to graphical models is developed for random graphs. In the second part of this thesis, we consider the situation in which there is an unknown graph that one wants to determine. This is a common occurrence since, in general, entities in the world are not directly observable, but must be inferred from some signal. We consider a general framework for uncovering these unknown graphs by a sequence of ‘tests’ or ‘questions’. We refer to this framework as graph discovery. In the third part of this thesis, we apply graph discovery to a problem in computer vision. To evaluate how well vision systems perform, their interpretations of imagery must be compared to the true ones. Often, image interpretations can be expressed as graphs; for example, vertices can represent objects and edges can represent relationships between objects. Thus, an image, before it is interpreted, corresponds to an unknown graph, and the interpretation of an image corresponds to graph discovery. In this work, we are interested in the evaluation of vision systems when these representation graphs are complex. We propose a visual Turing test for this purpose