No description available.

No description available.

No description available.

No description available.

The regular representation of the symmetric group Sn is a vector space of dimension n! with many interesting invariant subspaces. The projections of a vector onto these subspaces may be computed by first considering projections onto certain basis elements in the subspace and then recombining later. If all of these projections are kept, it creates an explosion in the size of the data, making it difficult to store and work with. This is a study of techniques to compress this computed data such that it is of the same dimmension as the original vector.

Thesis (Ph.D.)--University of Wyoming, 2007. / Title from PDF title page (viewed on June 17, 2009). Includes bibliographical references (p. 133-139).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 202-214). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S. in Computer Science) Naval Postgraduate School, March 1996. / Thesis advisor(s): Cynthia E. Irvine, Harold Fredricksen. "March 1996." Includes bibliography references (p. 37). Also available online.

no / The requirement to efficiently store and process SMILES data used in Chemoinformatics creates a demand for efficient techniques to compress this data. General-purpose transforms and compressors are available to transform and compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. We develop a transform specific to SMILES data that can be used alongside other general-purpose compressors as a preprocessor and post-processor to improve the compression of SMILES data. We test our transform with six other general-purpose compressors and also compare our results with another transform on our SMILES data corpus, we also compare our results with untransformed data.

This thesis addresses the design of quantizers for two-dimensional vectors, where the scalar components are quantized sequentially. Specifically, design algorithms for unrestricted polar quantizers (UPQ) and successively refinable UPQs (SRUPQ) for vectors in polar coordinates are proposed. Additionally, algorithms for the design of sequential scalar quantizers (SSQ) for vectors with correlated components in Cartesian coordinates are devised. Both the entropy-constrained (EC) and fixed-rate (FR) cases are investigated. The proposed UPQ and SRUPQ design algorithms are developed for continuous bivariate sources with circularly symmetric densities. They are globally optimal for the class of UPQs/SRUPQs with magnitude thresholds confined to a finite set. The time complexity for the UPQ design is $O(K^2 + KP_{max})$ in the EC case, respectively $O(KN^2)$ in the FR case, where $K$ is the size of the set from which the magnitude thresholds are selected, $P_{max}$ is an upper bound for the number of phase levels corresponding to a magnitude bin, and $N$ is the total number of quantization bins. The time complexity of the SRUPQ design is $O(K^3P_{max})$ in the EC case, respectively $O(K^2N^{'2}P_{max})$ in the FR case, where $N'$ denotes the ratio between the number of bins of the fine UPQ and the coarse UPQ. The SSQ design is considered for finite-alphabet correlated sources. The proposed algorithms are globally optimal for the class of SSQs with convex cells, i.e, where each quantizer cell is the intersection of the source alphabet with an interval of the real line. The time complexity for both EC and FR cases amounts to $O(K_1^2K_2^2)$, where $K_1$ and $K_2$ are the respective sizes of the two source alphabets. It is also proved that, by applying the proposed SSQ algorithms to finite, uniform discretizations of correlated sources with continuous joint probability density function, the performance approaches that of the optimal SSQs with convex cells for the original sources as the accuracy of the discretization increases. The proposed algorithms generally rely on solving the minimum-weight path (MWP) problem in the EC case, respectively the length-constrained MWP problem or a related problem in the FR case, in a weighted directed acyclic graph (WDAG) specific to each problem. Additional computations are needed in order to evaluate the edge weights in this WDAG. In particular, in the EC-SRUPQ case, this additional work includes solving the MWP problem between multiple node pairs in some other WDAG. In the EC-SSQ (respectively, FR-SSQ) case, the additional computations consist of solving the MWP (respectively, length-constrained MWP) problem for a series of other WDAGs. / Dissertation / Doctor of Philosophy (PhD)