It was hypothesized that the self-esteem instability and emotional reactivity associated with narcissism may be related to the simplicity of cognitive self-representation known as low self-complexity. The relationships among narcissism, self-concept, affect and violent behaviour were investigated in two studies with samples of federally sentenced violent and sexual offenders. In the first study, participants completed personality inventories and a measure of self-complexity, while changes in self-esteem were tracked across two weeks. In the second study, participants completed the same battery of measures as in the first study in addition to several new measures of anger, aggression and previous violent behaviour. Also, official records were consulted to obtain collateral information regarding violent behaviour. Experiences of positive and negative events and the resulting changes in affect and self-esteem were tracked over six weeks. It was expected that self-complexity would mediate reactivity to daily events such that individuals low in self-complexity and high in narcissistic personality traits would report the greatest shifts in self-esteem and emotion. When positive and negative self-complexity were considered separately, some support was found for the hypothesized buffering effect. Generally, higher positive self-complexity was associated with better coping while higher negative self-complexity was associated with less desirable reactions to events. Theoretical and clinical implications of this finding are discussed along with limitations of these studies and suggestions for future research.

The intensive search of optimal matching block greatly increases the complexity of motion estimation that constitutes the most time-consuming part of H.264/AVC. This thesis presents a novel fast hierarchical motion search (FHMS) strategy along with a heuristic early termination technique. The proposed FHMS paradigm is based on the observation that most search operations can be avoided by sub-sampling current macroblocks first to reduce the dimensions of both reference pictures and search range. The early termination establishes a statistical threshold that makes motion search for stationary or quasi-stationary areas to terminate earlier without sacrificing accuracy. Experimental results show the proposed FHMS is highly time-efficient in finding the motion vectors and at the same time maintaining satisfactory video quality.

published_or_final_version / abstract / toc / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

Pseudo-random sequences are a crucial component of cryptography, particularly in stream cipher design. In this thesis we will investigate several measures of randomness for certain classes of finitely generated sequences. We will present a heuristic algorithm for calculating the k-error linear complexity of a general sequence, of either finite or infinite length, and results on the closeness of the approximation generated. We will present an linear time algorithm for determining the linear complexity of a sequence whose characteristic polynomial is a power of an irreducible element, again presenting variations for both finite and infinite sequences. This algorithm allows the linear complexity of such sequences to be determined faster than was previously possible. Finally we investigate the stability of m-sequences, in terms of both k-error linear complexity and k-error period. We show that such sequences are inherently stable, but show that some are more stable than others.

In this thesis, we consider approaches to enumeration problems in the parameterized complexity setting. We obtain competitive parameterized algorithms to enumerate all, as well as several of, the solutions for two related problems Neighbour String and Kemeny Rank Aggregation. In both problems, the goal is to find a solution that is as close as possible to a set of inputs (strings and total orders, respectively) according to some distance measure. We also introduce a notion of enumerative kernels for which there is a bijection between solutions to the original instance and solutions to the kernel, and provide such a kernel for Kemeny Rank Aggregation, improving a previous kernel for the problem. We demonstrate how several of the algorithms and notions discussed in this thesis are extensible to a group of parameterized problems, improving published results for some other problems.

No description available.

No description available.

No description available.

Ternary Content Addressable Memory or in short TCAM is a special type of memory that can execute a certain set of operations in parallel on all of its words. Because of power consumption and relatively small storage capacity, it has only been used in special environments. Over the past few years its cost has been reduced and its storage capacity has increased signifi cantly and these exponential trends are continuing. Hence it can be used in more general environments for larger problems. In this research we study how to exploit its computational power in order to speed up fundamental problems and needless to say that we barely scratched the surface. The main problems that has been addressed in our research are namely Boolean matrix multiplication, approximate subset queries using bloom filters, Fixed universe priority queues and network flow classi cation. For Boolean matrix multiplication our simple algorithm has a run time of O (d(N^2)/w) where N is the size of the square matrices, w is the number of bits in each word of TCAM and d is the maximum number of ones in a row of one of the matrices. For the Fixed universe priority queue problems we propose two data structures one with constant time complexity and space of O((1/ε)n(U^ε)) and the other one in linear space and amortized time complexity of O((lg lg U)/(lg lg lg U)) which beats the best possible data structure in the RAM model namely Y-fast trees. Considering each word of TCAM as a bloom filter, we modify the hash functions of the bloom filter and propose a data structure which can use the information capacity of each word of TCAM more efi ciently by using the co-occurrence probability of possible members. And finally in the last chapter we propose a novel technique for network flow classi fication using TCAM.