An integrated functional mapping and source localization platform for brain research

The premise of this dissertation is to create a highly integrated platform that combines the most current recording technologies for brain research through the development of new algorithms for three-dimensional (3D) functional mapping and 3D source localization. The recording modalities that were integrated include: Electroencephalography (EEG), Optical Topographic Maps (OTM), Magnetic Resonance Imaging (MRI), and Diffusion Tensor Imaging (DTI). This work can be divided into two parts: The first part involves the integration of OTM with MRI, where the topographic maps are mapped to both the skull and cortical surface of the brain. This integration process is made possible through the development of new algorithms that determine the probes location on the MRI head model and warping the 2D topographic maps onto the 3D MRI head/brain model. Dynamic changes of the brain activation can be visualized on the MRI head model through a graphical user interface. The second part of this research involves augmenting a fiber tracking system, by adding the ability to integrate the source localization results generated by commercial software named Curry. This task involved registering the EEG electrodes and the dipole results to the MRI data. Such Integration will allow the visualization of fiber tracts, along with the source of the EEG, in a 3D transparent brain structure. The research findings of this dissertation were tested and validated through the participation of patients from Miami Children Hospital (MCH). Such an integrated platform presented to the medical professionals in the form of a user-friendly graphical interface is viewed as a major contribution of this dissertation. It should be emphasized that there are two main aspects to this research endeavor: (1) if a dipole could be situated in time at its different positions, its trajectory may reveal additional information on the extent and nature of the brain malfunction; (2) situating such a dipole trajectory with respect to the fiber tracks could ensure the preservation of these fiber tracks (axons) during surgical interventions, preserving as a consequence these parts of the brain that are responsible for information transmission.

Three essays on labor liberalization and its effects on world economy

This dissertation consists of three theoretical essays on immigration, international trade and political economy. The first two essays analyze the political economy of immigration in developed countries. The third essay explores new ground on the effects of labor liberalization in developing countries. Trade economists have witnessed remarkable methodological developments in mathematical and game theoretical models during the last seventy years. This dissertation benefits from these advances to analyze economic issues related to immigration. The first essay applies a long run general equilibrium trade model similar to Krugman (1980), and blends it with the median voter ala-Mayer (1984) framework. The second essay uses a short run general equilibrium specific factor trade model similar to Jones (1975) and incorporates it with the median voter model similar to Benhabib (1997). The third essay employs a five stage game theoretical approach similar to Vogel (2007) and solves it by the method of backward induction. The first essay shows that labor liberalization is more likely to come about in societies that have more taste for varieties, and that workers and capital owners could share the same positive stance toward labor liberalization. In a dynamic model, it demonstrates that the median voter is willing to accept fewer immigrants in the first period in order to preserve her domestic political influence in the second period threatened by the naturalization of these immigrants. The second essay shows that the liberalization of labor depends on the host country's stock and distribution of capital, and the number of groups of skilled workers within each country. I demonstrate that the more types of goods both countries produce, the more liberal the host country is toward immigration. The third essay proposes a theory of free movement of goods and labor between two economies with imperfect labor contracts. The heart of my analysis lies in the determinants of talent development where individuals' decisions to emigrate are related to the fixed costs of emigration. Finally, free trade and labor affect income via an indirect effect on individuals' incentives to invest in the skill levels and a direct effect on the prices of goods.

Contemporary aspects of dividends: Before and during the financial crisis

The number of dividend paying firms has been on the decline since the popularity of stock repurchases in the 1980s, and the recent financial crisis has brought about a wave of dividend reductions and omissions. This dissertation examined the U.S. firms and American Depository Receipts that are listed on the U.S. equity exchanges according to their dividend paying history in the previous twelve quarters. While accounting for the state of the economy, the firm’s size, profitability, earned equity, and growth opportunities, it determines whether or not the firm will pay a dividend in the next quarter. It also examined the likelihood of a dividend change. Further, returns of firms were examined according to their dividend paying history and the state of the economy using the Fama-French three-factor model. Using forward, backward, and step-wise selection logistic regressions, the results show that firms with a history of regular and uninterrupted dividend payments are likely to continue to pay dividends, while firms that do not have a history of regular dividend payments are not likely to begin to pay dividends or continue to do so. The results of a set of generalized polytomous logistic regressions imply that dividend paying firms are more likely to reduce dividend payments during economic expansions, as opposed to recessions. Also the analysis of returns using the Fama-French three factor model reveals that dividend paying firms are earning significant abnormal positive returns. As a special case, a similar analysis of dividend payment and dividend change was applied to American Depository Receipts that trade on the NYSE, NASDAQ, and AMEX exchanges and are issued by the Bank of New York Mellon. Returns of American Depository Receipts were examined using the Fama-French two-factor model for international firms. The results of the generalized polytomous logistic regression analyses indicate that dividend paying status and economic conditions are also important for dividend level change of American Depository Receipts, and Fama-French two-factor regressions alone do not adequately explain returns for these securities.

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.

Risk factors and associations for hepatitis C infection among Hispanic/Latino intravenous drug users in Miami-Dade County, Florida

Hepatitis C infection (HCV) continues to disproportionately affect Hispanics/Latinos in the United States. Hispanic/Latino intravenous drug users (IDUs), because of their risky injection and sexual behaviors, are prone to HCV infection and rapid transmission of the virus to others via several routes. With a prevalence rate of approximately 75% among IDUs, it is imperative that transmission of HCV be prevented in this population. This study aims to examine the associations between demographic, injection and sexual risk factors to HCV infection in a group Hispanic/Latino IDUs in Miami-Dade County, Florida. Preliminary unadjusted results in this sample reveal that age (OR=4.592, p=0.004), weekly injection (OR=5.171, p=0.000), daily injection frequency (OR=3.856, p=0.000) and use of a dirty needle (OR=2.320, p= 0.006) were all significantly associated with HCV infection. Being born outside the U.S. was significantly negatively associated with HCV infection (OR=0.349, p=0.004). Additionally, having two or more sex partners in the past three months (OR=0.472, p=0.014) was negatively associated with HCV infection. After adjusting for all other variables, older age (AOR=7.470, p=0.006), weekly injection (AOR=3.238, p=0.007) and daily injection frequency (AOR=2.625, p=0.010) were all significantly associated with HCV infection. Being born outside the U.S. (AOR=0.369, p=0.019) was a significant protective factor for HCV infection, along with having two or more sex partners in the past three months (AOR=0.481, p=0.037). When analyzing the significant variables in a backward regression model, having 2 or more sex partners in the past three months was not significant at the p

Are marginalized women being left behind? A population-based study of institutional deliveries in Karnataka, India

Background While India has made significant progress in reducing maternal mortality, attaining further declines will require increased skilled birth attendance and institutional delivery among marginalized and difficult to reach populations. Methods A population-based survey was carried out among 16 randomly selected rural villages in rural Mysore District in Karnataka, India between August and September 2008. All households in selected villages were enumerated and women with children 6 years of age or younger underwent an interviewer-administered questionnaire on antenatal care and institutional delivery. Results Institutional deliveries in rural areas of Mysore District increased from 51% to 70% between 2002 and 2008. While increasing numbers of women were accessing antenatal care and delivering in hospitals, large disparities were found in uptake of these services among different castes. Mothers belonging to general castes were almost twice as likely to have an institutional birth as compared to scheduled castes and tribes. Mothers belonging to other backward caste or general castes had 1.8 times higher odds (95% CI: 1.21, 2.89) of having an institutional delivery as compared to scheduled castes and tribes. In multivariable analysis, which adjusted for inter- and intra-village variance, Below Poverty Line status, caste, and receiving antenatal care were all associated with institutional delivery. Conclusion The results of the study suggest that while the Indian Government has made significant progress in increasing antenatal care and institutional deliveries among rural populations, further success in lowering maternal mortality will likely hinge on the success of NRHM programs focused on serving marginalized groups. Health interventions which target SC/ST may also have to address both perceived and actual stigma and discrimination, in addition to providing needed services. Strategies for overcoming these barriers may include sensitization of healthcare workers, targeted health education and outreach, and culturally appropriate community-level interventions. Addressing the needs of these communities will be critical to achieving Millennium Development Goal Five by 2015.

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

An examination of Bernard Connor's The History of Poland (1698) and its depiction of the political, religious, and cultural history of the Polish-Lithuanian commonwealth

Connor was an Irish-born member of seventeenth-century English medical society who made an impact on medicine through his use of anatomy. This forward-thinking scientist also worked as a court physician for the Polish king John III Sobieski (1629- 1696) and published a history of that country. This thesis will examine Bernard Connor's 1698 publication The History of Poland to show that the Commonwealth was considered a vision of a progressive European parliamentary government that could serve as a model for a struggling English parliamentary government, thus supporting Larry Wolff and Maria Todorova's vision of the later eighteenth-century creation of the idea of a backward "eastern Europe."

Caratterizzazione, analisi a ritroso e di stabilità attuale di uno scorrimento traslativo in Marnoso Arenacea (Casola Valsenio, RA)

Il lavoro di tesi consiste nella caratterizzazione, nell’analisi a ritroso e di stabilità attuale di un fenomeno franoso avvenuto a Casola Valsenio, in provincia di Ravenna. La caratterizzazione è comprensiva di analisi geomorfologica dell’area, indagine sismo-stratigrafica e indagine geotecnica di laboratorio. Quindi è stato definito il meccanismo di rottura della frana e sono stati cartografati gli elementi morfologici costituenti l’area di frana. L’indagine geofisica si basa su tecniche ad onde di superficie e include misure tromografiche di rumore sismico ambientale e misure attive multicanale. Queste forniscono, rispettivamente, le curve H/V e gli spettri di velocità di fase e di gruppo, dalle quali è possibile ricavare informazioni di carattere stratigrafico. La caratterizzazione fisico meccanica del materiale pelitico marnoso, prelevato sul piano di scorrimento basale, include analisi granulometrica, determinazione dei limiti di consistenza e test di taglio anulare per ottenere il valore di angolo di attrito residuo. Le informazioni ottenute dalle indagini hanno consentito di definire il modello geologico tecnico del fenomeno, che viene rappresentato rispetto a quattro sezioni, tre relative alla situazione attuale e una relativa al versante prefrana. Sulla base di questi sono state condotte delle analisi a ritroso, che hanno consentito di ricavare le condizioni idrauliche del versante al momento della rottura, per differenti combinazioni di geometria del cuneo e diagramma di spinta dell’acqua. Grazie ai risultati ottenuti in modalità backward, sono state effettuate le analisi di stabilità della scarpata attuale, aventi lo scopo di determinare la distanza orizzontale tra scarpata e frattura di trazione che determinerebbe una situazione di instabilità. Infine sono state condotte analisi di sensitività rispetto ai parametri geometrici del cuneo, di resistenza del materiale e di riempimento idraulico della frattura dii trazione.

Riconoscimento real-time di gesture tramite tecniche di machine learning

Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.

Biomechanical modeling of parotid glands morphing in head & neck radiation therapy treatments

Durante i trattamenti radioterapici dei pazienti oncologici testa-collo, le ghiandole parotidee (PGs) possono essere indebitamente irradiate a seguito di modificazioni volumetriche-spaziali inter/intra-frazione causate da fattori quali il dimagrimento, l’esposizione a radiazioni ionizzanti ed il morphing anatomico degli organi coinvolti nelle aree d’irraggiamento. Il presente lavoro svolto presso la struttura di Fisica Medica e di Radioterapia Oncologica dell’A.O.U di Modena, quale parte del progetto di ricerca del Ministero della Salute (MoH2010, GR-2010-2318757) “ Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments ”, sviluppa un modello biomeccanico in grado di rappresentare il processo di deformazione delle PGs, considerandone la geometria, le proprietà elastiche e l'evoluzione durante il ciclo terapeutico. Il modello di deformazione d’organo è stato realizzato attraverso l’utilizzo di un software agli elementi finiti (FEM). Molteplici superfici mesh, rappresentanti la geometria e l’evoluzione delle parotidi durante le sedute di trattamento, sono state create a partire dai contorni dell’organo definiti dal medico radioterapista sull’immagine tomografica di pianificazione e generati automaticamente sulle immagini di setup e re-positioning giornaliere mediante algoritmi di registrazione rigida/deformabile. I constraints anatomici e il campo di forze del modello sono stati definiti sulla base di ipotesi semplificative considerando l’alterazione strutturale (perdita di cellule acinari) e le barriere anatomiche dovute a strutture circostanti. L’analisi delle mesh ha consentito di studiare la dinamica della deformazione e di individuare le regioni maggiormente soggette a cambiamento. Le previsioni di morphing prodotte dal modello proposto potrebbero essere integrate in un treatment planning system per metodiche di Adaptive Radiation Therapy.

Performance improvement of broadband distributed Raman amplifier using bidirectional pumping with first and dual order forward pumps

In this paper, a new bidirectional pumping scheme with dual order forward pumps is proposed. Performance is compared numerically with conventional bidirectional and backward only pumping schemes for a 70 nm bandwidth, 61.5 km distributed Raman amplifier. We demonstrate that it is possible to design a flat gain spectrum with improved noise figure and OSNR, as well as a low gain ripple (<1 dB).

Seismic Response Analysis of a Full-Scale Base-Isolated Structure via Measurements and Modeling

The full-scale base-isolated structure studied in this dissertation is the only base-isolated building in South Island of New Zealand. It sustained hundreds of earthquake ground motions from September 2010 and well into 2012. Several large earthquake responses were recorded in December 2011 by NEES@UCLA and by GeoNet recording station nearby Christchurch Women's Hospital. The primary focus of this dissertation is to advance the state-of-the art of the methods to evaluate performance of seismic-isolated structures and the effects of soil-structure interaction by developing new data processing methodologies to overcome current limitations and by implementing advanced numerical modeling in OpenSees for direct analysis of soil-structure interaction.

This dissertation presents a novel method for recovering force-displacement relations within the isolators of building structures with unknown nonlinearities from sparse seismic-response measurements of floor accelerations. The method requires only direct matrix calculations (factorizations and multiplications); no iterative trial-and-error methods are required. The method requires a mass matrix, or at least an estimate of the floor masses. A stiffness matrix may be used, but is not necessary. Essentially, the method operates on a matrix of incomplete measurements of floor accelerations. In the special case of complete floor measurements of systems with linear dynamics, real modes, and equal floor masses, the principal components of this matrix are the modal responses. In the more general case of partial measurements and nonlinear dynamics, the method extracts a number of linearly-dependent components from Hankel matrices of measured horizontal response accelerations, assembles these components row-wise and extracts principal components from the singular value decomposition of this large matrix of linearly-dependent components. These principal components are then interpolated between floors in a way that minimizes the curvature energy of the interpolation. This interpolation step can make use of a reduced-order stiffness matrix, a backward difference matrix or a central difference matrix. The measured and interpolated floor acceleration components at all floors are then assembled and multiplied by a mass matrix. The recovered in-service force-displacement relations are then incorporated into the OpenSees soil structure interaction model.

Numerical simulations of soil-structure interaction involving non-uniform soil behavior are conducted following the development of the complete soil-structure interaction model of Christchurch Women's Hospital in OpenSees. In these 2D OpenSees models, the superstructure is modeled as two-dimensional frames in short span and long span respectively. The lead rubber bearings are modeled as elastomeric bearing (Bouc Wen) elements. The soil underlying the concrete raft foundation is modeled with linear elastic plane strain quadrilateral element. The non-uniformity of the soil profile is incorporated by extraction and interpolation of shear wave velocity profile from the Canterbury Geotechnical Database. The validity of the complete two-dimensional soil-structure interaction OpenSees model for the hospital is checked by comparing the results of peak floor responses and force-displacement relations within the isolation system achieved from OpenSees simulations to the recorded measurements. General explanations and implications, supported by displacement drifts, floor acceleration and displacement responses, force-displacement relations are described to address the effects of soil-structure interaction.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.