Modeling the Speed and Timing of American Sign Language to Generate Realistic Animations

While there are many Deaf or Hard of Hearing (DHH) individuals with excellent reading literacy, there are also some DHH individuals who have lower English literacy. American Sign Language (ASL) is not simply a method of representing English sentences. It is possible for an individual to be fluent in ASL, while having limited fluency in English. To overcome this barrier, we aim to make it easier to generate ASL animations for websites, through the use of motion-capture data recorded from human signers to build different predictive models for ASL animations; our goal is to automate this aspect of animation synthesis to create realistic animations. This dissertation consists of several parts: Part I, defines key terminology for timing and speed parameters, and surveys literature on prior linguistic and computational research on ASL. Next, the motion-capture data that our lab recorded from human signers is discussed, and details are provided about how we enhanced this corpus to make it useful for speed and timing research. Finally, we present the process of adding layers of linguistic annotation and processing this data for speed and timing research. Part II presents our research on data-driven predictive models for various speed and timing parameters of ASL animations. The focus is on predicting the (1) existence of pauses after each ASL sign, (2) predicting the time duration of these pauses, and (3) predicting the change of speed for each ASL sign within a sentence. We measure the quality of the proposed models by comparing our models with state-of-the-art rule-based models. Furthermore, using these models, we synthesized ASL animation stimuli and conducted a user-based evaluation with DHH individuals to measure the usability of the resulting animation. Finally, Part III presents research on whether the timing parameters individuals prefer for animation may differ from those in recordings of human signers. Furthermore, it also includes research to investigate the distribution of acceleration curves in recordings of human signers and whether utilizing a similar set of curves in ASL animations leads to measurable improvements in DHH users\u27 perception of animation quality

Financial asset returns, direction-of-change forecasting, and volatility dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1