261 research outputs found
Complementary Algorithms For Tableaux
We study four operations defined on pairs of tableaux. Algorithms for the
first three involve the familiar procedures of jeu de taquin, row insertion,
and column insertion. The fourth operation, hopscotch, is new, although
specialised versions have appeared previously. Like the other three operations,
this new operation may be computed with a set of local rules in a growth
diagram, and it preserves Knuth equivalence class. Each of these four
operations gives rise to an a priori distinct theory of dual equivalence. We
show that these four theories coincide. The four operations are linked via the
involutive tableau operations of complementation and conjugation.Comment: 29 pages, 52 .eps files for figures, JCTA, to appea
The Application of the Montage Image Mosaic Engine To The Visualization Of Astronomical Images
The Montage Image Mosaic Engine was designed as a scalable toolkit, written
in C for performance and portability across *nix platforms, that assembles FITS
images into mosaics. The code is freely available and has been widely used in
the astronomy and IT communities for research, product generation and for
developing next-generation cyber-infrastructure. Recently, it has begun to
finding applicability in the field of visualization. This has come about
because the toolkit design allows easy integration into scalable systems that
process data for subsequent visualization in a browser or client. And it
includes a visualization tool suitable for automation and for integration into
Python: mViewer creates, with a single command, complex multi-color images
overlaid with coordinate displays, labels, and observation footprints, and
includes an adaptive image histogram equalization method that preserves the
structure of a stretched image over its dynamic range. The Montage toolkit
contains functionality originally developed to support the creation and
management of mosaics but which also offers value to visualization: a
background rectification algorithm that reveals the faint structure in an
image; and tools for creating cutout and down-sampled versions of large images.
Version 5 of Montage offers support for visualizing data written in HEALPix
sky-tessellation scheme, and functionality for processing and organizing images
to comply with the TOAST sky-tessellation scheme required for consumption by
the World Wide Telescope (WWT). Four online tutorials enable readers to
reproduce and extend all the visualizations presented in this paper.Comment: 16 pages, 9 figures; accepted for publication in the PASP Special
Focus Issue: Techniques and Methods for Astrophysical Data Visualizatio
Basis descent methods for convex essentially smooth optimization with applications to quadratic/entropy optimization and resource allocation
Cover title.Includes bibliographical references (p. 33-38).Partially supported by the U.S. Army Research Office (Center for Intelligent Control Systems) DAAL03-86-K-0171 Partially supported by the National Science Foundation. NSF-ECS-8519058by Paul Tseng
Automatic input rectification
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 51-55).We present a novel technique, automatic input rectification, and a prototype implementation, SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. When given an atypical input that does not satisfy these constraints, SOAP automatically rectifies the input (i.e., changes the input so that it satisfies the learned constraints). The goal is to automatically convert potentially dangerous inputs into typical inputs that the program is highly likely to process correctly. Our experimental results show that, for a set of benchmark applications (Google Picasa, ImageMagick, VLC, Swfdec, and Dillo), this approach effectively converts malicious inputs (which successfully exploit vulnerabilities in the application) into benign inputs that the application processes correctly. Moreover, a manual code analysis shows that, if an input does satisfy the learned constraints, it is incapable of exploiting these vulnerabilities. We also present the results of a user study designed to evaluate the subjective perceptual quality of outputs from benign but atypical inputs that have been automatically rectified by SOAP to conform to the learned constraints. Specifically, we obtained benign inputs that violate learned constraints, used our input rectifier to obtain rectified inputs, then paid Amazon Mechanical Turk users to provide their subjective qualitative perception of the difference between the outputs from the original and rectified inputs. The results indicate that rectification can often preserve much, and in many cases all, of the desirable data in the original input.by Fan Long.S.M
Automatic Input Rectification
We present a novel technique, automatic input rectification, and a prototype implementation called SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. When given an atypical input that does not satisfy these constraints, SOAP automatically rectifies the input (i.e., changes the input so that is satisfies the learned constraints). The goal is to automatically convert potentially dangerous inputs into typical inputs that the program is highly likely to process correctly. Our experimental results show that, for a set of benchmark applications (namely, Google Picasa, ImageMagick, VLC, Swfdec, and Dillo), this approach effectively converts malicious inputs (which successfully exploit vulnerabilities in the application) into benign inputs that the application processes correctly. Moreover, a manual code analysis shows that, if an input does satisfy the learned constraints, it is incapable of exploiting these vulnerabilities. We also present the results of a user study designed to evaluate the subjective perceptual quality of outputs from benign but atypical inputs that have been automatically rectified by SOAP to conform to the learned constraints. Specifically, we obtained benign inputs that violate learned constraints, used our input rectifier to obtain rectified inputs, then paid Amazon Mechanical Turk users to provide their subjective qualitative perception of the difference between the outputs from the original and rectified inputs. The results indicate that rectification can often preserve much, and in many cases all, of the desirable data in the original input
Painted network flows with weighted divergence
The theory of network flows deals with problems which can be represented as networks and like linear programming provides a general framework for formulating and solving a considerable number of optimization problems. Many network problems can be recast as linear programming problems and vice-versa. A way to treat multistage processes possessing certain invariant aspects is dynamic programming. Certain of these can be recast as network problems;This dissertation is a discussion and detailed description of several algorithms designed to solve two classes of special network problems. One consists of network problems where the flow and the divergence are weighted. And the second class consists of network problems where the potential is weighted. Further, we look at a dynamic programming problem discussed and treated by Kulkarni (1981) and Bechhofer (1985) and formulate it as a network problem which is a special linear optimal distribution problem
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