The first essay empirically evaluates recently developed techniques that have been proposed to improve the estimation of affine term structure models. The evaluation presented here is performed on two dimensions. On the first dimension, I find that invariant transformations and rotations can be used to reduce the number of free parameters needed to estimate the model and subsequently, improve the empirical performance of affine term structure models. The second dimension of this evaluation surrounds the comparison between estimating an affine term structure model using the model-free method and the inversion method. Using daily LIBOR rate and swap rate quotes from June 1996 to July 2008 to extract a panel of 3,034 time-series observations and 14 cross sections, this paper shows that, a term structure model that is estimated using the model-free method does not perform significantly better in fitting yields, at any horizon, than the more traditional methods available in the literature.The second essay attempts explores implications of using principal components analysis in the estimation of affine term structure models. Early work employing principal component analysis focused on portfolio formation and trading strategies. Recent work, however, has moved the usage of principal components analysis into more formal applications such as the direct involvement of principal component based factors within an affine term structure model. It is this usage of principal components analysis in formal model settings that warrants a study of potential econometric implications of its application to term structure modeling. Serial correlation in interest rate data, for example, has been documented by several authors. The majority of the literature has focused on strong persistence in state variables as giving rise to this phenomena. In this paper, I take yields as given, and hence document the effects of whitening on the model-implied state-dependent factors, subsequently estimated by the principal component based model-free method. These results imply that the process of pre-whitening the data does play a critical role in model estimation. Results are robust to Monte Carlo Simulations. Empirical results are obtained from using daily LIBOR rate and swap rate quotes from June 1996 to July 2008 to extract a panel of zero-coupon yields consisting of 3,034 time-series observations and 14 cross sections.The third essay examines the extent to which the prevalence of estimation risk in numerical integration creates bias, inefficiencies, and inaccurate results in the widely used class of affine term structure models. In its most general form, this class of models relies on the solution to a system of non-linear Ricatti equations to back out the state-factor coefficients. Only in certain cases does this class of models admit explicit, and thus analytically tractable, solutions for the state factor coefficients. Generally, and for more economically plausible scenarios, explicit closed form solutions do not exist and the application of Runge-Kutta methods must be employed to obtain numerical estimates of the coefficients for the state variables. Using a panel of 3,034 yields and 14 cross-sections, this paper examines what perils, if any, exist in this trade off of analytical tractability against economic flexibility. Robustness checks via Monte Carlo Simulations are provided. In specific, while the usage of analytical methods needs less computational time, numerical methods can be used to estimate a broader set of economic scenarios. Regardless of the data generating process, the generalized Gaussian process seems to dominate the Vasicek model in terms of bias and efficiency. However, when the data are generated from a Vasicek model, the Vasicek model performs better than the generalized Gaussian process for fitting the yield curve. These results impart new and important information about the trade off that exists between using analytical methods and numerical methods for estimate affine term structure models.

abstract: This dissertation aimed to identify the factors that facilitated the friendship initiation, development, and maintenance between Taiwanese and Chinese students and the influential relationship among those factors. Nine Taiwanese and nine Chinese students studying at one Taiwanese university were recruited for this study. The Chinese students were in Taiwan for at least two years. The participants were friends with the other party for at least 8 months. This study was divided into three stages. In the first stage, participants were required to provide factors that facilitated their friendship with the other party. Fifty ideas were collected. In the second stage, participants were asked to clarify those factors and then categorize those factors. Fourteen categories were identified in this stage. The participants, then, voted on factors that affected their friendship formation, development, and maintenance with other party. Fifteen factors were voted the highest among those factors. Those 15 factors were imported into interpretive structure modeling (ISM) software for the next stage. In the third stage, 18 one-on-one interviews were conducted, and 18 ISM diagrams were generated. ISM provided a method to identify the influential relationship among those factors. According to the results, the friendship formation model was proposed. Five stages were identified in this model: exploring, matching, engaging, deepening and bonding. / Dissertation/Thesis / Doctoral Dissertation Communication 2016

Water is a complex liquid with many unusual properties. Our understanding of its physical, chemical and biological properties is greatly advanced after a century of dedicated research but there are still many unresolved questions. If answered, they could have important long-term consequences for practical applications ranging from drug design to water purification. This thesis presents results on the structure, dynamics and thermodynamics of liquid water. The focus is on theoretical simulations applied to interpret experimental data from mainly x-ray and neutron scattering and spectroscopy techniques. The structural sensitivity of x-ray and neutron diffraction is investigated using reverse Monte Carlo simulations and information on the pair-correlation functions of water is derived. A new method for structure modeling of computationally demanding data sets is presented and used to resolve an inconsistency between experimental extended x-ray absorption fine-structure and diffraction data regarding oxygen-oxygen pair-correlations. Small-angle x-ray scattering data are modeled using large-scale classical molecular dynamics simulations, and the observed enhanced scattering at supercooled temperatures is connected to the presence of a Widom line emanating from a liquid-liquid critical point in the deeply supercooled high pressure regime. An investigation of inherent structures reveals an underlying structural bimodality in the simulations connected to disordered high-density and ordered low-density molecules, providing a clearer interpretation of experimental small-angle scattering data. Dynamical anomalies in supercooled water observed in inelastic neutron scattering experiments, manifested by low-frequency collective excitations resembling a boson peak, are investigated and found to be connected to the thermodynamically defined Widom line. Finally, x-ray absorption spectra are calculated for simulated water structures using density functional theory. An approximation of intra-molecular zero-point vibrational effects is found to significantly improve the relative spectral intensities but a structural investigation indicates that the classical simulations underestimate the amount of broken hydrogen bonds. / Vatten är en komplex vätska med flera ovanliga egenskaper. Vår förståelse av dess fysiska, kemiska och biologiska egenskaper har utvecklats mycket sedan systematiska vetenskapliga studier började genomföras för mer än ett sekel sedan, men många viktiga frågor är fortfarande obesvarade. En ökad förståelse skulle på sikt kunna leda till framsteg inom viktiga områden så som medicinutveckling och vattenrening. Denna avhandling presenterar resultat kring vattnets struktur, dynamik och termodynamik. Fokusen ligger på teoretiska simuleringar som använts för att tolka experimentella data från huvudsakligen röntgen- och neutronspridning samt spektroskopier. Den strukturella känsligheten i röntgen- och neutrondiffraktionsdata undersöks via reverse Monte Carlo metoden och information om de partiella parkorrelationsfunktionerna erhålls. En ny metod för strukturmodellering av beräkningsintensiva data presenteras och används för att lösa en motsägelse mellan experimentell diffraktion och EXAFS angående syre- syre parkorrelationsfunktionen. Data från röntgensmåvinkelspridning modelleras med storskaliga klassiska molekyldynamiksimuleringar, och den observerade förhöjda småvinkelspridningen vid underkylda temperaturer kopplas till existensen av en Widomlinje härrörande från en vätske- vätske kritisk punkt i det djupt underkylda området vid höga tryck. En undersökning av inherenta strukturer i simuleringarna påvisar en underliggande strukturell bimodalitet mellan molekyler i oordnade högdensitetsregioner respektive ordnade lågdensitetsregioner, vilket ger en tydligare tolkning av den experimentella småvinkelspridningen. Dynamiska anomalier i underkylt vatten som har observerats i inelastisk neutronspridning, speciellt förekomsten av lågfrekventa excitationer som liknar en bosontopp, undersöks och kopplas till den termodynamiskt definierade Widomlinjen. Slutligen presenteras densitetsfunktionalberäkningar av röntgenabsorptionsspektra för simulerade vattenstrukturer. En approximation av intramolekylära nollpunktsvibrationseffekter förbättrar relativa intensiteteri spektrumen avsevärt, men en strukturanalys visar att klassiska simuleringar av vatten underskattar andelen brutna vätebindningar. / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 6: Submitted. Paper 7: Submitted. Paper 8: Manuscript. Paper 9: Submitted.

Chromatin is a complex polymer molecule in eukaryotic cells, primarily consisting of DNA and histones. Many works have shown that the 3D folding of chromatin structure plays an important role in DNA expression. The recently proposed Chro- mosome Conformation Capture technologies, especially the Hi-C assays, provide us an opportunity to study how the 3D structures of the chromatin are organized. Based on the data from Hi-C experiments, many chromatin 3D structure modeling methods have been proposed. However, there is limited ground truth to validate these methods and no robust chromatin structure alignment algorithms to evaluate the performance of these methods. In our work, we first made a thorough literature review of 25 publicly available population Hi-C-based chromatin 3D structure modeling methods. Furthermore, to evaluate and to compare the performance of these methods, we proposed a novel data simulation method, which combined the population Hi-C data and single-cell Hi-C data without ad hoc parameters. Also, we designed a global and a local alignment algorithms to measure the similarity between the templates and the chromatin struc- tures predicted by different modeling methods. Finally, the results from large-scale comparative tests indicated that our alignment algorithms significantly outperform the algorithms in literature.

Yes / We develop an intensity-based model of municipal yields, making simultaneous use of the CDS premiums of the insurers and both insured and uninsured municipal bond transactions. We estimate the model individually for 61 municipal issuers by exploiting the dramatic decline in credit quality of the bond insurers from July 2007 to June 2008, and decompose the municipal yield spread based on the estimated parameters. The decomposition reveals a dominant role of the liquidity component as well as interactions between liquidity and default similar to those modeled by Chen et al. (2016) for corporate bonds. Towards the end of the sample period, our model also reproduces the "yield inversion" phenomenon documented by Bergstresser et al. (2010).

Because the way mothers play with their children may have significant impacts on children's social, cognitive, and linguistic development, researchers have become interested in potential predictors of maternal play. In the present study, 40 mother–infant dyads were followed from child age 5–20 months. Five-month habituation rate and 13 and 20 month temperamental difficulty were found to be predictive of maternal play quality at 20 months. The most parsimonious theoretical model was one in which habituation was mediated by temperamental difficulty in predicting mother play. Consistent with prior speculation in the literature, these data support the possibility that mothers adjust some aspects of their play behaviors to fit their children's cognitive and temperamental capabilities.

The aim of the work presented in this thesis is to contribute to a fundamental understanding of polarizing phenomena in some scarab beetles. The aim is also to study the beetle structures as inspiration in fabrication of artificially sculptured films. The three investigated species Cetonia aurata, Potosia cuprea and Liocola marmorata are of the family Scarabaediae and subfamily Cetoniianae (Guldbaggar). They were all collected at Swedish locations and are the only species of Cetoniinae scarabs in Sweden. This work reports on their optical properties represented by Mueller matrix elements, degree of polarization data and trace curves in the Cartesian complex plane representation of polarized light. From these results we verifyan earlier structural model for the Cetonia aurata and make way for similar models of the other two species. The ellipsometer used in this work is of dual rotating compensator type from which the complete Mueller-matrix for the medium examined can be obtained. The ellipsometric measurements were conducted on the scutellum for four different angles of incidence, 45°, 55°, 65° and 75° over a wave-length range of 245-1000 nm. Common for all examined species is that left polarization is observed in the wavelength range of 400 800 nm. For most of these species the polarization state is close to circular at some wavelengths especially at smaller angles of incidence. In general the degree of polarization is high (above 50%) when the polarization is near-circular. The degree of polarization also shows a clear dependence on the angle of incidence. The earlier model for Cetonia aurata shows a good agreement with the experimental data of this work. The model is also found as a good basis to work from to create models for the other two species.

Dans cette thèse, nous proposons des modèles de reconstruction de la structure curviligne fondée sur la modélisation stochastique et sur un système d’apprentissage structuré. Nous supposons que le réseau de lignes, dans sa totalité, peut être décomposé en un ensemble de segments de ligne avec des longueurs et orientations variables. Cette hypothèse nous permet de reconstituer des formes arbitraires de la structure curviligne pour différents types de jeux de données. Nous calculons les descripteurs des caractéristiques curvilignes fondés sur les profils des gradients d’image et les profils morphologiques. Pour le modèle stochastique, nous proposons des contraintes préalables qui définissent l'interaction spatiale des segments de ligne. Pour obtenir une configuration optimale correspondant à la structure curviligne latente, nous combinons plusieurs hypothèses de ligne qui sont calculées par échantillonnage MCMC avec différents jeux de paramètres. De plus, nous apprenons une fonction de classement qui prédit la correspondance du segment de ligne donné avec les structures curvilignes latentes. Une nouvelle méthode fondée sur les graphes est proposée afin d’inférer la structure sous-jacente curviligne en utilisant les classements de sortie des segments de ligne. Nous utilisons nos modèles pour analyser la structure curviligne sur des images statiques. Les résultats expérimentaux sur de nombreux types de jeux de données démontrent que les modèles de structure curviligne proposés surpassent les techniques de l'état de l'art. / In this dissertation, we propose curvilinear structure reconstruction models based on stochastic modeling and ranking learning system. We assume that the entire line network can be decomposed into a set of line segments with variable lengths and orientations. This assumption enables us to reconstruct arbitrary shapes of curvilinear structure for different types of datasets. We compute curvilinear feature descriptors based on the image gradient profiles and the morphological profiles. For the stochastic model, we propose prior constraints that define the spatial interaction of line segments. To obtain an optimal configuration corresponding to the latent curvilinear structure, we combine multiple line hypotheses which are computed by MCMC sampling with different parameter sets. Moreover, we learn a ranking function which predicts the correspondence of the given line segment and the latent curvilinear structures. A novel graph-based method is proposed to infer the underlying curvilinear structure using the output rankings of the line segments. We apply our models to analyze curvilinear structure on static images. Experimental results on wide types of datasets demonstrate that the proposed curvilinear structure modeling outperforms the state-of-the-art techniques.

Phosphate is an essential nutrient for all forms of life. Escherichia coli has a PhoR/PhoB two component regulatory system that controls the expression of various genes whose products allow the cell to thrive in low phosphate environments. The signaling mechanism of the PhoR/PhoB system has been studied and the phosphorylation cascade that controls gene expression is well understood. What is still unknown is how PhoR senses the phosphate level of the environment. The PstS, PstC, PstA, PstB, and PhoU proteins play a role in this signal sensing. This work confirms the hypothesis that the PstSCAB complex senses the environmental phosphate and that phosphate signal is passed through PhoU to PhoR. Further, this work characterizes residues important for interaction on PhoU and PhoR and identifies a structural model for interaction. This model points to a potential mechanism for PhoU mediated signaling to PhoR. We tested this model with direct coupling analysis and obtained further confirmation. Further use of these techniques may elucidate more of the interactions necessary for proper phosphate signaling.

During the recent years, numerous so-called Buy Now, Pay Later companies have emerged. A type of financial institution offering short term consumer credit contracts. As these institutions have gained popularity, their undertaken credit risk has increased vastly. Simultaneously, the IFRS 9 regulatory requirements must be complied with. Specifically, the Probability of Default (PD) for the entire lifetime of such a contract must be estimated. The collection of incremental PDs over the entire course of the contract is called the PD term structure. Accurate estimates of the PD term structures are desirable since they aid in steering business decisions based on a given risk appetite, while staying compliant with current regulations. In this thesis, the efficiency of Machine Learning within PD term structure modeling is examined. Two categories of Machine Learning algorithms, in five variations each, are evaluated; (1) Deep Neural Networks; and (2) Gradient Boosted Trees. The Machine Learning models are benchmarked against a traditional Markov Chain model. The performance of the models is measured by a set of calibration and discrimination metrics, evaluated at each time point of the contract as well as aggregated over the entire time horizon. The results show that Machine Learning can be used efficiently within PD term structure modeling. The Deep Neural Networks outperform the Markov Chain model in all performance metrics, whereas the Gradient Boosted Trees are better in all except one metric. For short-term predictions, the Machine Learning models barely outperform the Markov Chain model. For long-term predictions, however, the Machine Learning models are superior. / Flertalet s.k. Köp nu, betala senare-företag har växt fram under de senaste åren. En sorts finansiell institution som erbjuder kortsiktiga konsumentkreditskontrakt. I samband med att dessa företag har blivit alltmer populära, har deras åtagna kreditrisk ökat drastiskt. Samtidigt måste de regulatoriska kraven ställda av IFRS 9 efterlevas. Specifikt måste fallisemangsrisken för hela livslängden av ett sådant kontrakt estimeras. Samlingen av inkrementell fallisemangsrisk under hela kontraktets förlopp kallas fallisemangsriskens terminsstruktur. Precisa estimat av fallisemangsriskens terminsstruktur är önskvärda eftersom de understödjer verksamhetsbeslut baserat på en given riskaptit, samtidigt som de nuvarande regulatoriska kraven efterlevs. I denna uppsats undersöks effektiviteten av Maskininlärning för modellering av fallisemangsriskens terminsstruktur. Två kategorier av Maskinlärningsalgoritmer, i fem variationer vardera, utvärderas; (1) Djupa neuronnät; och (2) Gradient boosted trees. Maskininlärningsmodellerna jämförs mot en traditionell Markovkedjemodell. Modellernas prestanda mäts via en uppsättning kalibrerings- och diskrimineringsmått, utvärderade i varje tidssteg av kontraktet samt aggregerade över hela tidshorisonten. Resultaten visar att Maskininlärning är effektivt för modellering av fallisemangsriskens terminsstruktur. De djupa neuronnäten överträffar Markovkedjemodellen i samtliga prestandamått, medan Gradient boosted trees är bättre i alla utom ett mått. För kortsiktiga prediktioner är Maskininlärningsmodellerna knappt bättre än Markovkedjemodellen. För långsiktiga prediktioner, däremot, är Maskininlärningsmodellerna överlägsna.