published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

No description available.

This thesis describes signal analysis methods for single-molecule fluorescence data. The primary factor motivating method development is the need to distinguish single-molecule FRET fluctuations due to conformational dynamics from fluctuations due to distance-independent FRET changes. Single-molecule Förster resonance energy transfer (FRET) promises a distinct advantage compared to alternative biochemical methods in its potential to relate biomolecular structure to function. Standard measurements assume that the mean transfer efficiency between two fluorescent probes, a donor and an acceptor, corresponds to the mean donor-acceptor distance, thus providing structural information. Accordingly, measurement analysis assumes that mean transfer efficiency fluctuations entail mean donor-acceptor distance fluctuations. Detecting such fluctuations is important in resolving molecular dynamics, as molecular function often necessitates structural changes. A problem arises, however, in that factors other than donor-acceptor distance changes may induce mean transfer efficiency fluctuations. We refer to these factors as distance-independent FRET changes. We present analysis methods to detect distance-independent photophysical dynamics and to determine their correlation with distance-dependent FRET dynamics. First, we review a theory of photon statistics and show how we can use the theory to detect FRET fluctuations. Second, we extend the theory to alternating laser excitation (ALEx) measurements and demonstrate how fluorophore stoichiometry, a measure of fluorophore brightness, reports on distance-independent photophysical dynamics. Next, we provide a measure to determine the extent to which stoichiometry fluctuations account for FRET dynamics. Finally, we use a framework similar to the preceding along with recent advances in the theory of total internal reflection fluorescence (TIRF) microscopy FRET measurements to detect TIRF FRET fluctuations which occur on a timescale faster than the measurement temporal resolution. We validate our methods with simulations and demonstrate their utility in delineating RNA polymerase open complex conformational dynamics.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

by Wai-sum Chan. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1986 / Includes bibliographical references.

Au Tsz-yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 76-79). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Forecasting Time Series --- p.1 / Chapter 1.2 --- Importance of Multiple Forecasts --- p.2 / Chapter 1.3 --- Methodology of Forecasting for Autoregressive Models --- p.3 / Chapter 1.4 --- Bootstrap Approach --- p.9 / Chapter 1.5 --- Objectives --- p.12 / Chapter 2 --- "Bootstrapping Simultaneous Prediction Intervals, Case A: p known" --- p.15 / Chapter 2.1 --- TS Procedure --- p.16 / Chapter 2.2 --- CAO Procedure --- p.18 / Chapter 2.3 --- MAS Procedure --- p.20 / Chapter 3 --- "Bootstrapping Simultaneous Prediction Intervals, Case B: p unknown" --- p.24 / Chapter 3.1 --- TS Procedure --- p.25 / Chapter 3.2 --- CAO Procedure --- p.27 / Chapter 3.3 --- MAS Procedure --- p.28 / Chapter 4 --- Simulation Study --- p.29 / Chapter 4.1 --- Design of The Experiment --- p.29 / Chapter 4.2 --- Simulation Results --- p.33 / Chapter 5 --- A Real-Data Case --- p.36 / Chapter 5.1 --- Case A --- p.37 / Chapter 5.2 --- Case B --- p.42 / Chapter 6 --- Conclusion --- p.46 / Chapter A --- Tables of Simulation Results for Case A --- p.49 / Chapter B --- Tables of Simulation Results for Case B --- p.62 / Chapter C --- References --- p.76

Wong Yiu Kei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Multiple Forecasting and Examples --- p.2 / Chapter 1.2 --- Previous Literature on Prediction Interval and Simultaneous Prediction Interval --- p.3 / Chapter 1.3 --- Objectives --- p.7 / Chapter 2 --- The Holt-Winters forecasting procedure --- p.8 / Chapter 2.1 --- Exponential Smoothing --- p.8 / Chapter 2.2 --- Holt-Winters Forecasting Procedure --- p.10 / Chapter 2.2.1 --- Additive Holt-Winters Model --- p.14 / Chapter 2.2.2 --- Multiplicative Holt-Winters Model --- p.16 / Chapter 2.3 --- Some Practical Issues --- p.18 / Chapter 2.3.1 --- Choosing Starting Values --- p.19 / Chapter 2.3.2 --- Choosing the Smoothing Parameters --- p.20 / Chapter 3 --- Constructing Simultaneous Prediction Intervals Method --- p.23 / Chapter 3.1 --- Bonferroni Procedure --- p.24 / Chapter 3.2 --- The 'Exact' Procedure --- p.25 / Chapter 3.3 --- Summary --- p.25 / Chapter 3.4 --- Covariance of forecast errors --- p.26 / Chapter 3.4.1 --- Yar and Chatfield's approach --- p.26 / Chapter 3.4.2 --- "Koehler, Snyder and Ord Approach" --- p.28 / Chapter 3.5 --- Simulation Study --- p.31 / Chapter 4 --- An Illustrative Example --- p.37 / Chapter 5 --- Simulation --- p.56 / Chapter 5.1 --- Conclusion --- p.62 / Appendix --- p.64 / References --- p.68

Time is a critical element for the understanding of natural processes (e.g., earthquakes and weather) or human-made artifacts (e.g., stock market and speech signals). The analysis of time series, the result of sequentially collecting observations of such processes and artifacts, is becoming increasingly prevalent across scientific and industrial applications. The extraction of non-trivial features (e.g., patterns, correlations, and trends) in time series is a critical step for devising effective time-series mining methods for real-world problems and the subject of active research for decades. In this dissertation, we address this fundamental problem by studying and presenting computational methods for efficient unsupervised learning of robust feature representations from time series. Our objective is to (i) simplify and unify the design of scalable and accurate time-series mining algorithms; and (ii) provide a set of readily available tools for effective time-series analysis. We focus on applications operating solely over time-series collections and on applications where the analysis of time series complements the analysis of other types of data, such as text and graphs. For applications operating solely over time-series collections, we propose a generic computational framework, GRAIL, to learn low-dimensional representations that natively preserve the invariances offered by a given time-series comparison method. GRAIL represents a departure from classic approaches in the time-series literature where representation methods are agnostic to the similarity function used in subsequent learning processes. GRAIL relies on the attractive idea that once we construct the data-to-data similarity matrix most time-series mining tasks can be trivially solved. To overcome scalability issues associated with approaches relying on such matrices, GRAIL exploits time-series clustering to construct a small set of landmark time series and learns representations to reduce the data-to-data matrix to a data-to-landmark points matrix. To demonstrate the effectiveness of GRAIL, we first present domain-independent, highly accurate, and scalable time-series clustering methods to facilitate exploration and summarization of time-series collections. Then, we show that GRAIL representations, when combined with suitable methods, significantly outperform, in terms of efficiency and accuracy, state-of-the-art methods in major time-series mining tasks, such as querying, clustering, classification, sampling, and visualization. Overall, GRAIL rises as a new primitive for highly accurate, yet scalable, time-series analysis. For applications where the analysis of time series complements the analysis of other types of data, such as text and graphs, we propose generic, simple, and lightweight methodologies to learn features from time-varying measurements. Such applications often organize operations over different types of data in a pipeline such that one operation provides input---in the form of feature vectors---to subsequent operations. To reason about the temporal patterns and trends in the underlying features, we need to (i) track the evolution of features over different time periods; and (ii) transform these time-varying features into actionable knowledge (e.g., forecasting an outcome). To address this challenging problem, we propose principled approaches to model time-varying features and study two large-scale, real-world, applications. Specifically, we first study the problem of predicting the impact of scientific concepts through temporal analysis of characteristics extracted from the metadata and full text of scientific articles. Then, we explore the promise of harnessing temporal patterns in behavioral signals extracted from web search engine logs for early detection of devastating diseases. In both applications, combinations of features with time-series relevant features yielded the greatest impact than any other indicator considered in our analysis. We believe that our simple methodology, along with the interesting domain-specific findings that our work revealed, will motivate new studies across different scientific and industrial settings.