The extensive collection of Iron Age pottery from antiquarian investigations of Atlantic Roundhouse sites in Caithness, Sutherland and the Small Isles (Inner Hebrides) provided an ideal opportunity to address several gaps in the academic understanding of pottery sequences outwith the Western Isles (Outer Hebrides). Until now no work of this kind for Caithness or Sutherland has been conducted, and the material culture of Skye and the Inner Hebrides has been subsumed largely into the broader sequences of their more westerly neighbours. The aim of the thesis is twofold. Firstly, to establish pottery sequences for three sub-regions of Atlantic Scotland; Northern Mainland, Western Mainland and Skye and Small Isles, using both antiquarian material and pottery from recent excavations. This comprises the first five chapters of the thesis. And secondly, within the following three chapters, utilizing two pieces of experimental research and a series of case studies, the author explores the social narrative of the ceramic assemblage, ultimately to better understand technological and cultural aspects of pot making and use.

Li, Qiwei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 75-85). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Repetitive DNA Sequence --- p.3 / Chapter 1.1.1 --- Definition and Categorization of Repeti- tive DNA Sequence --- p.3 / Chapter 1.1.2 --- Definition and Categorization of Tandem Repeats --- p.4 / Chapter 1.1.3 --- Definition and Categorization of Interspersed Repeats --- p.6 / Chapter 1.2 --- Research Significance --- p.7 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Thesis Organization --- p.11 / Chapter 2 --- Literature Review and Overview of Our Method --- p.13 / Chapter 2.1 --- Existing Methods --- p.14 / Chapter 2.2 --- Overview of Our Method --- p.17 / Chapter 3 --- Theoretical Background --- p.22 / Chapter 3.1 --- Multinomial Distributions --- p.23 / Chapter 3.2 --- Dirichlet Distribution --- p.23 / Chapter 3.3 --- Metropolis-Hastings Sampling --- p.25 / Chapter 3.4 --- Gibbs Sampling --- p.26 / Chapter 4 --- Problem Description --- p.28 / Chapter 4.1 --- Generative Model --- p.29 / Chapter 4.1.1 --- Input Data R --- p.31 / Chapter 4.1.2 --- Parameters A (Repeat Segment Starting Positions) --- p.32 / Chapter 4.1.3 --- Parameters S (Repeat Segment Structures) --- p.33 / Chapter 4.1.4 --- Parameters θ(Motif Matrix) --- p.35 / Chapter 4.1.5 --- Parameters Φ (Background Distribution) . --- p.36 / Chapter 4.1.6 --- An Example of the Model Schematic Di- agram --- p.37 / Chapter 4.2 --- Parameter Structure --- p.38 / Chapter 4.3 --- Posterior Distribution --- p.40 / Chapter 4.3.1 --- The Full Posterior Distribution --- p.41 / Chapter 4.3.2 --- The Collapsed Posterior Distribution --- p.42 / Chapter 4.4 --- Conclusion --- p.43 / Chapter 5 --- Methodology --- p.45 / Chapter 5.1 --- Schematic Procedure --- p.46 / Chapter 5.1.1 --- The Basic Schematic Procedure --- p.46 / Chapter 5.1.2 --- The Improved Schematic Procedure --- p.47 / Chapter 5.2 --- Initialization --- p.49 / Chapter 5.3 --- Predictive Update Step for θn and Φn --- p.50 / Chapter 5.4 --- Gibbs Sampling Step for an --- p.50 / Chapter 5.5 --- Metropolis-Hastings Sampling Step for sn --- p.51 / Chapter 5.5.1 --- Rear Indel Move --- p.53 / Chapter 5.5.2 --- Partial Shift Move --- p.56 / Chapter 5.5.3 --- Front Indel Move --- p.56 / Chapter 5.6 --- Phase Shifts --- p.57 / Chapter 5.7 --- Conclusion --- p.58 / Chapter 6 --- Results and Discussion --- p.60 / Chapter 6.1 --- Settings --- p.61 / Chapter 6.2 --- Experiment on Synthetic Data --- p.63 / Chapter 6.3 --- Experiment on Real Data --- p.69 / Chapter 7 --- Conclusion and Future Work --- p.72 / Chapter 7.1 --- Conclusion --- p.72 / Chapter 7.2 --- Future Work --- p.74 / Bibliography --- p.75

Perturbation techniques are useful in the design of low complexity adaptive antenna arrays for estimating the gradient required in stochastic descent algorithms. Implementing projected perturbation sequences in an adaptive array allows the simultaneous reception of signals and the adaptation of the array weights while preserving the constraints imposed on the array weights.This thesis quantifies the performance of narrowband adaptive array processors that employ projected perturbation techniques. For different perturbation receiver structures the performance is determined under idealised conditions and importantly also when practical implementation issues are taken into account.The arrays performance is characterised by analysing the transient performance of the weight covariance matrix and by determining the misadjustment. By drawing similarities between two established analysis techniques a new misadjustment analysis technique is introduced.Practical implementation can impact on the arrays performance such that the benefit of the projected perturbation approach is lost. By characterising the array's sensitivity to perturbation noise additional projections which counteract some implementation effects are identified. The level of loss of performance due to weight quantisation and the limited dynamic range of the array weights is determined.

The reason for data mining is to generate rules that can be used as the basis for making decisions. One such area is sequence mining which, in terms of transactional datasets, can be stated as the discovery of inter-transaction associations or associations between different transactions. The data used for sequence mining is not limited to data stored in overtly temporal or longitudinally maintained datasets and in such domains data can be viewed as a series of events, or episodes, occurring at specific times. The problem thus becomes a search for collections of events that occur frequently together. While the mining of frequent episodes is an important capability, the manner in which such episodes interact can provide further useful knowledge in the search for a description of the behaviour of a phenomenon but as yet has received little investigation. Moreover, while many sequences are associated with absolute time values, most sequence mining routines treat time in a relative sense, returning only patterns that can be described in terms of Allen-style relationships (or simpler), ie. nothing about the relative pace of occurrence. They thus produce rules with a more limited expressive power. Up to this point in time temporal interval patterns have been based on the endpoints of the intervals, however in many cases the ‘natural’ point of reference is the midpoint of an interval and it is therefore appropriate to develop a mechanism for reasoning between intervals when midpoint information is known. This thesis presents a method for discovering interacting episodes from temporal sequences and the analysis of them using temporal patterns. The mining can be conducted both with and without the mechanism for handling the pace of events and the analysis is conducted using both the traditional interval algebras and a midpoint algebra presented in this thesis. The visualisation of rules in data mining is a large and dynamic field in its own right and although there has been a great deal of research in the visualisation of associations, there has been little in the area of sequence or episodic mining. Add to this the emerging field of mining stream data and there is a need to pursue methods and structures for such visualisations, and as such this thesis also contributes toward research in this important area of visualisation.

Group comparison per se is a fundamental task in many scientific endeavours but is also the basis of any classifier. Comparing groups of sequence data is a relevant task. To contrast sequence groups, we define Emerging Sequences (ESs) as subsequences that are frequent in sequences of one group and less frequent in another, and thus distinguishing sequences of different classes. There are two challenges to distinguish sequence classes by ESs: the extraction of ESs is not trivially efficient and only exact matches of sequences are considered. In our work we address those problems by a suffix tree-based framework and a sliding window matching mechanism. A classification model based on ESs is also proposed. Evaluating against several other learning algorithms, the experiments on two datasets show that our similar ESs-based classification model outperforms the baseline approaches. With the ESs' high discriminative power, our proposed model achieves satisfactory F-measures on classifying sequences.

Thesis (M.M.)--University of Wisconsin--Madison, 1955. / Typescript. Title from title screen (viewed Feb. 21, 2007). Includes bibliographical references (leaves 122-127). Online version of the print original.

A (periodic) correlation function is an important measure to evaluate the effectiveness of pseudorandom sequences. In practice, the sequences are required to have the impulse-like autocorrelation function. Also, crosscorrelation magnitudes of the distinct sequences must be as low as possible. Theoretically, the correlation of sequences has a strong connection with problems of algebraic coding and combinatorics. Namely, the correlation distribution of sequences is related to the weight distribution of codewords in algebraic codes. Furthermore, binary sequences with ideal two-level autocorrelation are equivalent to cyclic Hadamard difference sets in combinatorics. Therefore, a good knowledge of coding theory and combinatorics is helpful for a study of sequences with low correlation. In this thesis, the correlation of binary sequences is studied by the aid of fruitful results of coding theory and combinatorics. From this study, some interesting properties are presented on the correlation of binary sequences, and several new binary sequences with low correlation are discovered. The outline of thesis is as follows. First of all, crosscorrelation properties of binary sequences with ideal two-level autocorrelation are studied. The 3- and 5-valued crosscorrelation properties of several classes of binary sequences with ideal two-level autocorrelation are completely determined by either theoretical proofs or conjectures. Second, new binary sequences of period of a multiple of 4 are proposed by making use of the interleaved structure of sequences. The sequences have three-valued out-of-phase autocorrelation, i.e., {0, -4, +4}, which is optimal with respect to autocorrelation magnitude. Third, a new binary sequence family with low correlation and a large family size is proposed and its asymptotic optimality is examined. With respect to maximum correlation, a family size, and a linear complexity, the sequence family is competitive among all known binary sequence families. Finally, the construction of quadratic bent functions of a special polynomial form is presented by providing necessary and sufficient conditions on the coefficients of the polynomial. The resulting quadratic bent functions can be employed for the linear feedback shift register (LFSR) implementation of a family of bent sequences, which is a potential candidate for future code-division multiple access (CDMA) system.

Much of the existing academic discourse surrounding opening title sequences suggests that they function primarily by providing viewers with information concerning a program’s characters, settings, genre and themes. Such accounts seemingly fail to recognize more nuanced concurrent functions. Utilizing the concept of paratexts originally proposed by Gerard Genette in combination with a neoformalist approach to analysis, this project identifies patterns, narrative components, stylistic elements and various industrial and authorial characteristics within the field of American televisual opening title sequences in order to explore some of these underlying concomitant functions, and classify the segments that perform them accordingly.

In this thesis, we show that several natural questions about automatic sequences can be expressed as logical predicates and then decided mechanically. We extend known results in this area to broader classes of sequences (e.g., paperfolding words), introduce new operations that extend the space of possible queries, and show how to process the results. We begin with the fundamental concepts and problems related to automatic sequences, and the corresponding numeration systems. Building on that foundation, we discuss the general logical framework that formalizes the questions we can mechanically answer. We start with a first-order logical theory, and then extend it with additional predicates and operations. Then we explain a slightly different technique that works on a monadic second- order theory, but show that it is ultimately subsumed by an extension of the first-order theory. Next, we give two applications: critical exponent and paperfolding words. In the critical exponent example, we mechanically construct an automaton that describes a set of rational numbers related to a given automatic sequence. Then we give a polynomial-time algorithm to compute the supremum of this rational set, allowing us to compute the critical exponent and many similar quantities. In the paperfolding example, we extend our mechanical procedure to the paperfolding words, an uncountably infinite collection of infinite words. In the following chapter, we address abelian and additive problems on automatic sequences. We give an example of a natural predicate which is provably inexpressible in our first-order theory, and discuss alternate methods for solving abelian and additive problems on automatic sequences. We close with a chapter of open problems, drawn from the earlier chapters.

A (periodic) correlation function is an important measure to evaluate the effectiveness of pseudorandom sequences. In practice, the sequences are required to have the impulse-like autocorrelation function. Also, crosscorrelation magnitudes of the distinct sequences must be as low as possible. Theoretically, the correlation of sequences has a strong connection with problems of algebraic coding and combinatorics. Namely, the correlation distribution of sequences is related to the weight distribution of codewords in algebraic codes. Furthermore, binary sequences with ideal two-level autocorrelation are equivalent to cyclic Hadamard difference sets in combinatorics. Therefore, a good knowledge of coding theory and combinatorics is helpful for a study of sequences with low correlation. In this thesis, the correlation of binary sequences is studied by the aid of fruitful results of coding theory and combinatorics. From this study, some interesting properties are presented on the correlation of binary sequences, and several new binary sequences with low correlation are discovered. The outline of thesis is as follows. First of all, crosscorrelation properties of binary sequences with ideal two-level autocorrelation are studied. The 3- and 5-valued crosscorrelation properties of several classes of binary sequences with ideal two-level autocorrelation are completely determined by either theoretical proofs or conjectures. Second, new binary sequences of period of a multiple of 4 are proposed by making use of the interleaved structure of sequences. The sequences have three-valued out-of-phase autocorrelation, i.e., {0, -4, +4}, which is optimal with respect to autocorrelation magnitude. Third, a new binary sequence family with low correlation and a large family size is proposed and its asymptotic optimality is examined. With respect to maximum correlation, a family size, and a linear complexity, the sequence family is competitive among all known binary sequence families. Finally, the construction of quadratic bent functions of a special polynomial form is presented by providing necessary and sufficient conditions on the coefficients of the polynomial. The resulting quadratic bent functions can be employed for the linear feedback shift register (LFSR) implementation of a family of bent sequences, which is a potential candidate for future code-division multiple access (CDMA) system.