367 research outputs found
SAT and CP: Parallelisation and Applications
This thesis is considered with the parallelisation of solvers which search for either an arbitrary, or an optimum, solution to a problem stated in some formal way. We discuss the parallelisation of two solvers, and their application in three chapters.In the first chapter, we consider SAT, the decision problem of propositional logic, and algorithms for showing the satisfiability or unsatisfiability of propositional formulas. We sketch some proof-theoretic foundations which are related to the strength of different algorithmic approaches. Furthermore, we discuss details of the implementations of SAT solvers, and show how to improve upon existing sequential solvers. Lastly, we discuss the parallelisation of these solvers with a focus on clause exchange, the communication of intermediate results within a parallel solver. The second chapter is concerned with Contraint Programing (CP) with learning. Contrary to classical Constraint Programming techniques, this incorporates learning mechanisms as they are used in the field of SAT solving. We present results from parallelising CHUFFED, a learning CP solver. As this is both a kind of CP and SAT solver, it is not clear which parallelisation approaches work best here. In the final chapter, we will discuss Sorting networks, which are data oblivious sorting algorithms, i. e., the comparisons they perform do not depend on the input data. Their independence of the input data lends them to parallel implementation. We consider the question how many parallel sorting steps are needed to sort some inputs, and present both lower and upper bounds for several cases
Iterated Belief Revision Under Resource Constraints: Logic as Geometry
We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots.
The proposed memory architecture---called the universal memory architecture (UMA)---maintains an epistemic state in the form of a system of default rules similar to those studied by Pearl and by Goldszmidt and Pearl (systems Z and Z+). A duality between the category of UMA representations and the category of the corresponding model spaces, extending the Sageev-Roller duality between discrete poc sets and discrete median algebras provides a two-way dictionary from inference to geometry, leading to immense savings in computation, at a cost in the quality of representation that can be quantified in terms of topological invariants. Moreover, the same framework naturally enables comparisons between different model spaces, making it possible to analyze the deficiencies of one model space in comparison to others.
This paper develops the formalism underlying UMA, analyzes the complexity of maintenance and inference operations in UMA, and presents some learning guarantees for different UMA-based learners. Finally, we present simulation results to illustrate the viability of the approach, and close with a discussion of the strengths, weaknesses, and potential development of UMA-based learners
Topological Foundations of Cognitive Science
A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers:
** Topological Foundations of Cognitive Science, Barry Smith
** The Bounds of Axiomatisation, Graham White
** Rethinking Boundaries, Wojciech Zelaniec
** Sheaf Mereology and Space Cognition, Jean Petitot
** A Mereotopological Definition of 'Point', Carola Eschenbach
** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel
** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda
** Defining a 'Doughnut' Made Difficult, N .M. Gotts
** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts
** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi
** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Ki
Foundations of Software Science and Computation Structures
This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems
Foundations of Software Science and Computation Structures
This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems
Morphology of Mock SDSS Catalogues
We measure the geometry, topology and morphology of the superclusters in mock
SDSS catalogues prepared by Cole et al.(1998). The mock catalogues refer to
CDM and \LCDM {\em flat} cosmological models and are populated by
galaxies so that these act as biased tracers of mass, conforming with the
correlation function measured using APM catalogue. We compute the Minkowski
Functionals (MFs) for the cosmic density fields using SURFGEN (Sheth et
al.2003) and use the available 10 realizations of CDM to study the effect
of cosmic variance in estimation of MFs and Shapefinders, which we find to be
extremely well constrained statistics. Although all the mock catalogues of
galaxies have the same two-point correlation function and similar clustering
amplitude, the global MFs due to CDM show systematically lower amplitude
compared to those due to \LCDM; an indirect, but detectable effect due to
nonzero, higher order correlation functions. The characteristic thickness (T),
breadth (B) and length (L) of the superclusters are measured using the
available 10 realizations of CDM. While TB and T, B[1,17]
hMpc, we find the top 10 superclusters to be as long as 90 hMpc,
with the longest superclusters identified at percolation to be rare objects
with their length as large as 150 hMpc. The CDM superclusters are
found to be significantly longer than those in \LCDM. Thickness (T), breadth
(B), planarity (P) and mass/volumeweighted planarity and filamentarity of
the superclusters are found to be useful to compare the two models (abridged).Comment: 23 Pages, 12 Figures, MNRAS Style. Minor modifications to the text.
New references adde
Tools and Algorithms for the Construction and Analysis of Systems
This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems
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The design and analysis of boundary data structures
The thesis is concerned with the efficient interrogation of CAD data. CAD data finds use in diverse range of applications which necessitates extension and integration of the CAD data base. By an exhaustive categorization of such application requirements and analysis of various CAD techniques, it is shown that boundary data structures are the most suitable in CAD, CAM and advanced robotic applications.
Several boundary data structures have been proposed since the classic Winged edge data structure, these aimed at reducing the storage requirement and increasing information retrieval speeds. In this thesis methodologies are developed which enable us to discover compact and fast access time schemes and analyze and fine tune for individual applications. We demonstrate how the application of the optimality concepts can lead us to the discovery of more efficient data structures than popular data structures. All the boundary data structures proposed to date have been based on the underlying assumption that all the data resides in main memory. We show that in an integrated CAD environment (characterized by virtual a memory environment or a data base environment), these data structures are inefficient in both storage and time. We propose a new data structure shaped like A which is the most compact as well as more efficient in access time, under certain conditions of real memory and virtual memory. Experiments reveal a paradoxical phenomenon: access time increases with storage, violating the classic law of storage vs. time.
Recently non-manifold boundary geometric modeling has become popular to meet the growing needs such as uniform treatment of wire frame, surface and solid modeling and design by features. We introduce a uniform terminology and notation to distinguish and critically analyze several non-manifold boundary data structures. It is hoped to fulfill the need for a ready reference for the design of efficient boundary data structures. The other aspects dealt with are the validity and conversion of Boundary data structures.
To verify the concepts developed, in practice, a whole suite of fast algorithms have been implemented for model manipulation, visualization and data conversion
Parallelizing Timed Petri Net simulations
The possibility of using parallel processing to accelerate the simulation of Timed Petri Nets (TPN's) was studied. It was recognized that complex system development tools often transform system descriptions into TPN's or TPN-like models, which are then simulated to obtain information about system behavior. Viewed this way, it was important that the parallelization of TPN's be as automatic as possible, to admit the possibility of the parallelization being embedded in the system design tool. Later years of the grant were devoted to examining the problem of joint performance and reliability analysis, to explore whether both types of analysis could be accomplished within a single framework. In this final report, the results of our studies are summarized. We believe that the problem of parallelizing TPN's automatically for MIMD architectures has been almost completely solved for a large and important class of problems. Our initial investigations into joint performance/reliability analysis are two-fold; it was shown that Monte Carlo simulation, with importance sampling, offers promise of joint analysis in the context of a single tool, and methods for the parallel simulation of general Continuous Time Markov Chains, a model framework within which joint performance/reliability models can be cast, were developed. However, very much more work is needed to determine the scope and generality of these approaches. The results obtained in our two studies, future directions for this type of work, and a list of publications are included
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