Graphical model, the marriage between graph theory and probability theory, has been drawing increasing attention because of its many attractive features. In this paper, we consider the problem of learning the structure of graphical model based on observed data through a greedy forward-backward algorithm and with the use of learned model to classify the data into different categories. We establish the graphical model associated with a binary Ising Markov random field. And model selection is implemented by adding and deleting edges between nodes. Our experiments show that: compared with previous methods, the proposed algorithm has better performance in terms of correctness rate and model selection. / text

In cardiac biomechanics, people have been developing a complete model of the patient-specific heart. A finite element bi-ventricular model involves several critical steps. First is the acquisition of patient-specific heart geometry. Second is the definition of material model and its constitutive parameters which is suitable to model the behavior of heart muscle. Third is the integration of fiber orientation of myocardium into the bi-ventricular model. The first objective of this study is to investigate some significant aspects in ventricular biomechanics using a simple model of prolate spheroidal left ventricle (LV). These critical aspects include the geometry of LV, the material model, constitutive parameters and fiber orientations. Results of this simplified model are useful in developing a patient-specific model. For example, parametric study of hyper-elastic material is instructive in determining constitutive parameters of myocardium in a patient-specific model. The second objective of this study is to develop a workflow of building a patient-specific bi-ventricular model. It involves working with experimental data like CT images, DTMRI data and so on. A user defined Fung material model is also reviewed in detail. Two methods of assigning fiber orientation are discussed. Finally, the report points out the future work needed to get a valid patient-specific model which can be useful in research and clinical case. / text

Cyberspace has dramatically improved our daily lives in the past several decades. Meanwhile, people’s personal identifiable information (PII) is exposed online and is at risk of identity theft and cybercrimes. The Identity Ecosystem developed by the Center for Identity in the University of Texas at Austin addresses this problem and provides a statistical framework for understanding the value, risk and mutual relationships of PII. The Identity Ecosystem currently uses a general Bayesian Network Model to simulate the relationships among PII, which may be quite inaccurate for specific groups of people. This thesis proposes a solution that specializes the Bayesian Network used for particular groups of people. Both one-dimension specialization and multi-dimension specialization are investigated. Research problems like how to choose specialization criterion, how to set specialization boundaries, and how to overcome the difficult of insufficient data, are carefully studied. Specialization functionality is demonstrated based on empirical data. Finally, experiments of specialization are conducted on data obtained from online stories. This work is important in the sense that it provides a guide-line of designing more accurate models of PII within the Identity Ecosystem. / text

Recent advancements in control systems, such as the ones used in missile technology in the military or autonomous vehicle development have motivated this study in an attempt to explore various control algorithms and their implementation relevant those applications. Both missile interceptor and autonomous vehicle technology require precise and responsive control system to accurately determine the projectile path of pursuer to strike a moving target or reach a static finish line.The objective of this study is to investigate the performance of several control techniques for a mobile robot to autonomously track and pursue a moving object. Computer model is developed to numerically predict the path taken by the pursuer as it tracks an object moving in regular or random manner. In the computer simulation, the robot's path is calculated using three different techniques: reactive controller, linear estimation, and artificial neural network. Fitness of each method may be determined by evaluating the controller against several factors, such as interception time, steady-state positional error, steady-state time (settling time) and algorithm complexity, listed in decreasing order of importance. A working experimental model is developed to validate the controller selection determined from the computer model simulation. In the experimental setting, the primary inputs to the robot are visual images from cameras. The experiments are carried out with the robot receiving visual inputs from two different perspectives, overhead and frontal vision. Robust image processing technique becomes a topic of significant importance for the system. To manipulate visual images in real-time from raw inputs to comprehensible data, while maintaining fast computational time is a challenge that is addressed in this study. The results from computer simulations show that artificial neural network is a more powerful control algorithm, capable of estimating the object's path more accurately than the other two controllers, resulting in smaller steady-state positional error. The experimental results confirm this conclusion as artificial neural network outperforms the reactive and linear controller by intercepting the object more quickly, i.e. shorter interception time. / text

The main intention of component system is to retain interfaces which obtain in the model of proceeding between IS reference architecture components. Component model – it new type model, which helps to identify data systems components and their information connections. The use of component method’s allows to produce each component apart from all system’s, beyond realizing them so, that they have opportunity to operate in the allotment frame, to modernize whatever component and leave inviolate all others. Designer information system is implemented in one of Bank “Snoras” branch office, it’s Alytus subsidiary.Leading activity products are data, which is receivable from ready money dispenser, pay terminal and them service software filing and analyzing. All gathered data is stored in a database. Informacion is systematized and presented for users in semblance of forms or reports.

The main purpose of this work is to study model category structures (in the sense of Quillen) on the categories of small categories and small symmetric multicategories enriched over an arbitrary monoidal model category. Among these model structures, there is one of the greatest importance in applications. We call it the Dwyer-Kan model structure (for enriched categories or enriched symmetric multicategories), and a large amount of this work is dedicated to establishing it for different choices of monoidal model categories. Another model structure that we study is what we call the fibred model structure, again for both small categories and small symmetric multicategories enriched over a suitable monoidal model category. / The other purpose of this work is to study model category structures on the category of comonoids in a monoidal model category.

Compared to the previous twenty years, residential investments in the US appear more stable after the mid-1980s. Chapter 2 explores key hypotheses regarding the underlying causes. In particular, it uses estimated DSGE models to examine whether a more responsive interest rate policy stabilizes the housing market by keeping inflation in check. These estimations indeed found a policy that has become more responsive over time. Counter-factual analysis confirms that the change stabilizes inflation as well as nominal interest rate. It does not, however, find the change in policy to have stabilizing effect on real economic activity including housing investment. It finds that smaller TFP shocks make modest contributions, while the biggest contributing factor to the fall in the housing volatility is a reduction in the sensitivity of the investment to demand variations. Chapter 3 constructs a richly specified model for the housing market to examine the empirical relevance of various costs and frictions, including the investment adjustment cost, sticky construction costs, search frictions, and sluggish adjustment of house prices. Using the US national-level quarterly data from 1985 and 2007, we find that the gradual adjustment of house prices is the most important and irreplaceable feature of the model. The key to developing an optimization-based empirical housing model, therefore, is to provide a structural interpretation for the slow adjustment in house prices. Chapter 4 uses US national-level time series of residential investment, price index of new houses, consumption and interest rate to explore whether the US, as a nation, experienced a drop in the price elasticity of supply of new housing. Maximum likelihood estimations with a simple stock-and-flow model found a statistically significant drop of the elasticity from 10 to 2.2, when the quarterly data between 1971 and 2007 are split at 1985. A richer model with mechanisms of gradual adjustment also indicates such a reduction, when existing knowledge about the adjustment parameters is incorporated in the analysis. For the Federal Reserve, an inelastic supply can be a source of concern, because policy-driven demand in housing market is more likely to trigger undesirable swings in prices.

Aquatic propulsion by means of undulatory movements of the median (dorsal and anal) fins is the primary mode of transport for the Picasso triggerfish (Rhinecanthus aculeatus). Known as balistiform locomotion, this form of propulsion is an adaptation for highly efficient movement within complex environments such as coral reefs. A principle component of balistiform locomotion has been the development of momentum enhancement, a fin-force multiplier that increases swimming efficiency. This study examines the kinematics and energetics of balistiform locomotion employing theoretical models of thrust, power, and efficiency. Thrust and power were calculated and compared with theoretical values modeled by Lighthill and Blake (1990). This model has heretofore not been thoroughly vetted and was tested for accuracy and applicability. Thrust force was estimated from resistance (drag) using a vertical dead drop to determine terminal velocity; power was calculated from oxygen consumption measurements at different speeds. The Lighthill and Blake (1990) model requires median fin kinematics (frequency, wavelength, amplitude, wave angle), which were measured from high-speed videography and followed statistically predicted trends with frequency being the dominant variable, and the others changing little or not at all with speed. Momentum enhancement was found to be 3.6, close to Lighthill and Blake’s (1990) theoretically predicted value of 2.5. Momentum enhancement is experimentally proven here for the first time. Theoretical and empirical thrust force values are closely matched; theoretical thrust is greater at lower speeds and lower at higher speeds. The ratio of theoretical thrust to drag-estimated thrust averages 1.08. Theoretical values for power are greater than those measured by a factor of about 3.6 and cannot be explained by measurement error.

The goal of this dissertation is to examine the theoretical and empirical implications of the inventory theoretic approach to the demand for money. Chapter 1 reviews the existing inventory theoretic frameworks and empirical money demand literature and provides an overview of this thesis. One of the main conclusions is that the elasticity results from the existing inventory theoretic models are not robust. Chapter 2 develops a partial equilibrium inventory theoretic model, in which a fixed cost is involved per cash transfer. The key feature is that a firm endogenously chooses the frequency of pay periods, which a household takes as given. When the firm must borrow working capital and pay wages by cheque, I show that both the firm and the household choose to transfer cash every payday only. The model keeps the basic result from the classical inventory theoretic approach that both the income and interest elasticity of money demand are 0.5. Chapter 3 extends the partial equilibrium model into a general equilibrium framework and shows that the partial equilibrium elasticity results no longer apply in the general equilibrium. First, the income elasticity is 1 in the general equilibrium. Second, the interest elasticity has two values depending on a threshold interest rate. When interest rates are below this threshold, the model is the Cash-In-Advance model with a constant income velocity of money and zero interest elasticity; otherwise the interest elasticity is close to 0.5 and the velocity fluctuates in response to variations in interest rates. Finally, the general equilibrium elasticity results are robust across alternative specifications of the agent's utility. Chapter 4 calibrates the general equilibrium model to the last 40 years of US data for M1. By constructing a residual measure of money transaction costs from the structural money demand function, I find that a structural break in the transaction costs occurred in 1981 might have been responsible for the instability of long-run money demand. The benefit of this approach is that it can explain this pattern of money demand without appealing to an exogenous structural break in the money demand function.

In this project, the angles of miniature rose petals throughout the course of their bloom were measured with a digital protractor constructed using a function plotting software. The angle was then modeled as a function of time, and an analogy was drawn from the blooming of rose petals to the logistic growth of populations. The digital method of measurement and the logistic growth model were also applied to cyclamen and hyacinth petals.