This study aims to analyze the beta and risk behavior of the Swedish listed real estate stocks. Such a study will provide a clearer picture for investors and researchers about the changing nature of that behavior over time. The research method is based on descriptive statistics and CAPM beta regression analysis of the monthly returns. Correlation analysis is employed to identify diversification benefits within the sector stocks. In order to understand the behavior of beta/riskiness over time, the stationary and time-varying beta estimations are conducted using CAPM market excess-return model and rolling windows technique. In this investigation, the time period from 2003 to 2012 is analyzed. The results reveal that a) the betas of real estate stocks are asymmetric over time such that their values are higher during market upturns than in market downturns, b) the betas for the various types of real estate stocks are different, and c) there are low correlation coefficients among returns of real estate stocks, and within the various property type stock groups. While the real estate stock index as a whole is highly correlated to the market and has relatively stable betas over time, there are diversification benefits among Swedish real estate stocks. Hence, understanding the changing behaviors of beta over time of the various property type stocks can help investors optimize their market timing and cost of capital expectations according to the investment horizon. It is important to notice that a lot of capital for real estate equity investments in Sweden is allocated through non-traded private equity real estate funds. Therefore, transforming these private funds into real estate traded funds might add the data depth and the market efficiency necessary for better research validity and investment optimization. There are currently very few traded real estate securities in the Swedish market.

Due to the increase in the advances in wireless communication, there has been an increase in the use of multi-agents systems to complete any given task. In various applications, multi-agent systems are required to solve an underlying optimization problem to obtain the best possible solution within a feasible region. Solving such multi-agent optimization problems in a distributed framework preferable over centralized frameworks as the former ensures scalability, robustness, and security. Further distributed optimization problem becomes challenging when the decision variables of the individual agents are coupled. In this thesis, a distributed optimization problem with coupled constraints is considered, where a network of agents aims to cooperatively minimize the sum of their local objective functions, subject to individual constraints. This problem setup is relevant to many practical applications like formation flying, sensor fusion, smart grids, etc. For practical scenarios, where agents can solve their local optimal solution efficiently and require fewer assumptions on objective functions, the Alternating Direction Method of Multipliers(ADMM)-based approaches are preferred over gradient-based approaches. For such a constraint coupled problem, several distributed ADMM algorithms are present that guarantee convergence to optimality but they do not discuss the complete analysis for the rate of convergence. Thus, the primary goal of this work is to improve upon the convergence rate of the existing state-of-the-art Tracking-ADMM (TADMM) algorithm to solve the above-distributed optimization problem. Moreover, the current analysis in literature does not discuss the convergence in the case of a time-varying communication network. The first part of the thesis focuses on improving the convergence rate of the Tracking-ADMM algorithm to solve the above-distributed optimization problem more efficiently. To this end, an upper bound on the convergence rate of the TADMM algorithm is derived in terms of the weight matrix of the network. To achieve faster convergence, the optimal weight matrix is computed using a semi-definite programming (SDP) formulation. The improved convergence rate of this Fast-TADMM (F-TADMM) is demonstrated with a simple yet illustrative, coupled constraint optimization problem. Then, the applicability of F-TADMM is demonstrated to the problem of distributed optimal control for trajectory generation of aircraft in formation flight. In the second part of the thesis, the convergence analysis for TADMM is extended while considering a time-varying communication network. The modified algorithm is named as Time-Varying Tracking (TV-TADMM). The formal guarantees on asymptotic convergence are provided with the help of control system analysis of a dynamical system that uses Lyapunov-like theory. The convergence of this TV-TADMM is demonstrated on a simple yet illustrative, coupled constraint optimization problem with switching topology and is compared with the fixed topology setting.

Indiana University-Purdue University Indianapolis (IUPUI) / A popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such as Alzheimer's disease and autism. Information about complex association structure in high-dimensional fMRI data is often discarded by a calculating an average across complex spatiotemporal processes without providing an uncertainty measure around it. First, we propose a non-parametric approach to estimate the uncertainty of dynamic FC (dFC) estimates. Our method is based on three components: an extension of a boot strapping method for multivariate time series, recently introduced by Jentsch and Politis (2015); sliding window correlation estimation; and kernel smoothing. Second, we propose a two-step approach to analyze and summarize dFC estimates from a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with avors. In the first step, we apply our method from the first paper to estimate dFC for each region subject combination. In the second step, we use semiparametric additive mixed models to account for complex correlation structure and model dFC on a population level following the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined as the mutually exclusive division of brain regions into blocks with intra-connectivity greater than the one obtained by chance. As a result, we obtain brain partition suggesting the existence of common functionally-based brain organization. The main contribution of our work stems from the combination of the methods from the fields of statistics, machine learning and network theory to provide statistical tools for studying brain connectivity from a holistic, multi-disciplinary perspective.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19186号 / 工博第4063号 / 新制||工||1627(附属図書館) / 32178 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 小山田 耕二, 教授 小林 哲生, 教授 中村 裕一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Purpose: The purpose of this study was to investigate the effect physical activity has on an attempt to quit smoking, and whether this effect varies over time and/or is mediated by other variables. Previous research has demonstrated that cravings to smoke are associated with higher odds of relapse, and that the strength of this effect increases throughout a quit attempt. A bout of physical activity has also been shown to reduce cravings to smoke. We hypothesized that physical activity would have a time-varying effect on smoking mediated by cravings to smoke. Methods: The Wisconsin Smokers Health study was a randomized, placebo-controlled trial of five smoking cessation therapies. Each day, participants measured their steps with a pedometer and used their cell phone to complete Ecological Momentary Assessments of their cravings to smoke. We analyzed data from 7-days prior to a target quit day through 7-days following quit day. Participants were grouped by their daily step count and considered “less active” if they averaged less than 10,000 steps per day. Results: We found evidence among less physically active smokers that 1,000 additional steps per day on the first 1-3 days of a quit period is associated with a lower odds of smoking relapse on those days. This effect remained significant after including covariates in subsequent models, as well as both the effects of cravings and negative affect on smoking. We were also able to replicate the previous finding in this dataset that the effect of cravings to smoke on smoking odds increases during the first week post-quit day as time from quit day increases. Despite our finding that steps per day was associated with lower relapse among inactive smokers, and that inactive smokers had higher cravings and higher relapse rates, our study did not yield evidence for our mediation hypothesis that steps per day would associate with lower cravings to smoke. Conclusion: These findings could be used to tailor smoking cessation interventions to people at high risk of relapse and failure to successfully quit. A bout of 1,000 steps is roughly a 15-minute walk. Encouraging this kind of behavior when cravings are high could lead to successful quitting for less active smokers. Future cessation research with a focus on framing physical exercise as a protective tool against smoking relapse could be valuable for creating more efficient interventions. / Public Health

A smart grid cost/benefit analysis answers a series of economic questions that address the incremental benefits of each stage or decision point. Each stage of the economic analysis provides information about the incremental benefits of that stage with respect to the previous stage. With this approach stages that provide little or no economic benefits can be identified. In this study there are series of applications,-including quasi-steady state power flows over time-varying loads and costs of service, Monte Carlo simulations, reconfiguration for restoration, and coordinated control - that are used to evaluate the cost-benefits of a series of smart grid investments. In the electric power system planning process, engineers seek to identify the most cost-effective means of serving the load within reliability and power quality criteria. In order to accurately assess the cost of a given project, the feeder losses must be calculated. In the past, the feeder losses were estimated based upon the peak load and a calculated load factor for the year. The cost of these losses would then be calculated based upon an expected, fixed per-kWh generation cost. This dissertation presents a more accurate means of calculating the cost of losses, using hourly feeder load information and time-varying electric energy cost data. The work here attempts to quantify the improvement in high accuracy and presents an example where the economic evaluation of a planning project requires the more accurate loss calculation. Smart grid investments can also affect response to equipment failures where there are two types of responses to consider -blue-sky day and storm. Storm response and power restoration can be very expensive for electric utilities. The deployment of automated switches can benefit the utility by decreasing storm restoration hours. The automated switches also improve system reliably by decreasing customer interruption duration. In this dissertation a Monte Carlo simulation is used to mimic storm equipment failure events, followed by reconfiguration for restoration and power flow evaluations. The Monte Carlo simulation is driven by actual storm statistics taken from 89 different storms, where equipment failure rates are time varying. The customer outage status and durations are examined. Changes in reliability for the system with and without automated switching devices are investigated. Time varying coordinated control of Conservation Voltage Reduction (CVR) is implemented. The coordinated control runs in the control center and makes use of measurements from throughout the system to determine control settings that move the system toward optimum performance as the load varies. The coordinated control provides set points to local controllers. A major difference between the coordinated control and local control is the set points provided by the coordinated control are time varying. Reduction of energy and losses of coordinated control are compared with local control. Also eliminating low voltage problems with coordinated control are addressed. An overall economic study is implemented in the final stage of the work. A series of five evaluations of the economic benefits of smart grid automation investments are investigated. Here benefits that can be quantified in terms of dollar savings are considered here referred to as "hard dollar" benefits. Smart Grid investment evaluations to be considered include investments in improved efficiency, more cost effective use of existing system capacity with automated switches, and coordinated control of capacitor banks and voltage regulators. These Smart Grid evaluations are sequentially ordered, resulting in a series of incremental hard dollar benefits. Hard dollar benefits come from improved efficiency, delaying large capital equipment investments, shortened storm restoration times, and reduced customer energy use. The evaluation shows that when time varying loads are considered in the design, investments in automation can improve performance and significantly lower costs resulting in "hard dollar" savings. / Ph. D.

Dynamic processes on real-world networks are time-delayed due to finite processing speeds and the need to transmit data over nonzero distances. These time-delays often destabilize the network's dynamics, but are difficult to analyze because they increase the dimension of the network.We present results outlining an alternative means of analyzing these networks, by focusing analysis on the Lipschitz matrix of the relatively low-dimensional undelayed network. The key criteria, intrinsic stability, is computationally efficient to verify by use of the power method. We demonstrate applications from control theory and neural networks.

Ever faster data transmission in wireless communication is desired to satisfy emerging markets for various media services, such as voice, picture and video calls, multimedia messaging, music and video downloads, and even television. With the explosive increase in the use of mobile devices such as cellular phones, PDAs, GPS, and laptop computers, power consumption has become a prime consideration in the design of mobile communication systems. In order to reliably maintain a high rate of transmission and low power consumption, it is imperative that the receiver obtains as much knowledge as possible about the current state of the channel. A more accurate model of wireless communication channels will indisputably help in obtaining more knowledge about the transient channel state, providing a more accurate and efficient reproduction of the transmitted signal, and decreased power consumption by the receiver. With careful choice and consideration of the channel model, systemic optimization based on the selected channel model will improve the system performance of the transmitter and receiver through better encoding and decoding, as well as through better control of transmitted signal's power level. This thesis focuses on understanding the physical and statistical characteristics of wireless channels, and investigates how to represent wireless channels using simple mathematical models. This thesis initially studied a simple time-varying stationary channel, i.e.a multipath fiat fading channel without terminal motion, which is typically used for indoor wireless communication. With an introduction of stochastic differential equations, we derived a first-order AR stochastic process to represent this stationary channel. For a general multipath fiat fading channel with terminal motion, the traditional Clarke's model was then extended by incorporating the effects of fluctuations in the component phases and analyzed statistically. The resulting theoretical power spectrum was shown to fit practical measured spectra, in contrast to the traditional theoretical fiat fading channel spectra (Jakes' spectrum in [19]) . Finally, we developed a state-space model that represents a wireless channel using these modified spectral characteristics. This was achieved by developing a relationship between the state-space model and the theory of a rational transfer function. A novel method for designing a rational transfer function for linear systems was then proposed. In this method, the rational transfer function is represented via the Observable Canonical Form (OCF) to obtain the state-space model, which can be used to represent and simulate a fiat fading wireless channel. The presented state-space approach is simple and provides rapid computation. The present AR and state-space models provide valuable contributions that can be integrated with other algorithms for better system optimization of wireless communication networks. / Thesis / Doctor of Philosophy (PhD)

The U.S. forest products industry has witnessed an unprecedented period of mergers and acquisitions in the last decades. The overall goal of this thesis is to examine the impact of these activities on the financial performance and market power of the U.S. forest products industry in the last several decades. The first part of this thesis evaluated the mergers by event study. The results revealed that the equity market reacted positively to these mergers; the position of a firm and the relative transaction size explained most of the variations of the cumulative abnormal returns; and the risk for most of the selected 14 acquiring firms had changed after the mergers. The second part examined the market power of the U.S. paper industry by the new empirical industrial organization approach. The results indicated that the oligopoly power remained significant at the 1% level over the whole sample period; whereas the oligopsony power had dropped dramatically and become insignificant at the 5% level in recent 30 years.

Traditional and commercial speech synthesizers are incapable of synthesizing speech with proper emotion or prosody. Conveying prosody in artificially synthesized speech is difficult because of extreme variability in human speech. An arbitrary natural language sentence can have different meanings, depending upon the speaker, speaking style, context, and many other factors. Most concatenated speech synthesizers use phonemes, which are phonetic units defined by the International Phonetic Alphabet (IPA). The 50 phonemes in English are standardized and unique units of sound, but not expression. An earlier work proposed the analogy between speech and music ? ?speech is music, music is speech.? The speech data obtained from the master practitioners, who are trained in kinesensic voice, is marked on a five level intonation scale, which is similar to the music scale. From this speech data, 1324 unique expressive units, called expressemes®, are identified. The expressemes consist of melody and rhythm, which, in digital signal processing, is analogous to pitch, duration and energy of the signal. The expressemes have less acoustic and phonetic variability than phonemes, so they better convey the prosody. The goal is to develop a speech synthesizer which exploits the prosodic content of expressemes in order to synthesize expressive speech, with a small speech database. To create a reasonably small database that captures multiple expressions is a challenge because there may not be a complete set of speech segments available to create an emotion. Methods are suggested whereby acoustic mathematical modeling is used to create missing prosodic speech segments from the base prosody unit. New concatenatedormant hybrid speech synthesizer architecture is developed for this purpose. A pitch-synchronous time-varying frequency-warped wavelet transform based prosody manipulation algorithm is developed for transformation between prosodies. A time-varying frequency-warping transform is developed to smoothly concatenate the temporal and spectral parameters of adjacent expressemes to create intelligible speech. Additionally, issues specific to expressive speech synthesis using expressemes are resolved for example, Ergodic Hidden Markov Model based expresseme segmentation, model creation for F0 and segment duration, and target and join cost calculation. The performance of the hybrid synthesizer is measured against a commercially available synthesizer using objective and perceptual evaluations. Subjects consistently rated the hybrid synthesizer better in five different perceptual tests. 70% of speakers rated the hybrid synthesis as more expressive, and 72% preferred it over the commercial synthesizer. The hybrid synthesizer also got a comparable mean opinion score.