Gravitational Lensing : Models and Astrophysical Applications

채규현 University of Pittsburgh 1999 해외박사

This dissertation is an investigation of gravitational lens models and detailed analyses of the Tensing properties of three astrophysically interesting multiply-imaged quasi-stellar objects (QSOs) Q2237+0305, Q0957+561, and H1413+1143. A general-form power-law elliptical mass distribution is considered as a model for a Tensing object, and a Fourier series expansion technique is applied to obtain semi-analytical expressions for the deflection and magnification due to the general power-law mass model. Investigating more sophisticated lens models permitted by most recent and comprehensive sets of data, the following astrophysical/cosmological results are derived. For Q2237+0305, Tensing properties of the elliptical mass model with varying radial index are calculated, and similarities and quantitative differences between the elliptical lens model and a pseudo-elliptical lens model (a power-law sphere plus shear) are highlighted. For Q0957+561, the elliptical mass model and a power-law sphere are used to model the Tensing galaxy and cluster respectively, as motivated by recent Hubble Space Telescope (HST) imaging of the galaxy and weak Tensing observations of the cluster. The lens model for Q0957+561 gives a best-fit to present observational constraints and yields a Hubble constant of H_(o) = 57^(+19)_(-16) (95% confidence) km s^(-1) Mpc^(-1). For H1413+1143, recent data from HST WFPC1,2, NICMOS, and FOS are used to explore possible lens models. Analyses of two-component lens models (namely, two-galaxy and galaxy+cluster) indicate that at least the continuum source region is microlensed. The broad emission line region could also be microlensed. However, information from present observations are not sufficient to draw any conclusions on the leasing nature of the broad emission line region or its size scale.

파라미트릭 점프 프로세스를 위한 최적의 모수 추정 방법론 : Optimal Calibration for Parametric Jump Processes

양승호 포항공과대학교 일반대학원 2011 국내박사

According to numerous empirical evidences observed in option markets, it is clear that the celebrating Black-Scholes-Merton option pricing model can not explain the intrinsic properties of option prices in real markets such as the implied volatility smile behavior. To capture the smile effect many option pricing models or methods have been developed in a non-parametric and parametric way. In non-parametric approaches they do not rely on pre-assumed models but instead try to uncover/induce the model. There is a weak point with non-parametric approaches which it cannot applied to pricing path-dependent exotic options due to its lack of underlying dynamics. Recently in financial literature parametric methods, such as exponential L´evy models and affine jump-diffusion models, have been widely adopted as alternative models that explain stylized facts of asset returns and volatility smile effects of traded option prices. Hence if the parameters are calibrated reasonably the parametric models can be very powerful. Unfortunately the number of parameters is a lot and it’s hard to estimate parameters from the information in financial market. To calibrate them we use cross-sectional data of option prices. Least-square sense is usually employed to calibrate them in finance, although it is well-known ill-posed inverse problem. To conquer the ill-posed inverse problem we propose a derivative-free calibration method constrained by four observable statistical moments (mean, variance, skewness and kurtosis) from underlying time series and so-called multi-basin system which consists of three sequential phases to expedite the search for a good parameter set. To verify the performance of the proposed methods, we conduct simulations on some model-generated option prices data and real-world option market data. The simulation results show that the proposed methods fit the option ranges well and calibrate the parameter set of exponential L´evy models and affine jump-diffusion models reasonably and robustly. In this thesis we also give a modularized summary of all the detailed equations relevant to all exponential L´evy models and affine jumpdiffusion models in a consistent way by using the unified notations.

Ordinary Differential Equation Multi-Domain Models of MEMS Structures

Childs, Carter J The Pennsylvania State University ProQuest Dissert 2023 해외박사(DDOD)

Modeling is a pivotal part of any modern design process. The ability to accurately predict designs before building cuts down on development costs leading to faster development cycles. Modeling of transducers has been accomplished in various methods, mostly being compartmentalized into mechanical, electrical, or system models. While each of these models is important, the current solutions require substantial interaction between model types. There exists headroom to expand the system-level models to be less dependent on the subsystem model responses. To accomplish this, techniques developed before comprehensive Finite Element Analysis tools are leveraged and updated to work within modern computational tools, while expanding the repertoire to include modern production techniques of Micro-Electrical-Mechanical-Systems (MEMS). The invention of transmission line models that model the response of physical systems commonly found in MEMS sensory devices was undertaken. The models were built in Mathworks Simscape multidomain modeling software, taking advantage of the multiple domain definitions. The models developed can be used to allow system models to work independently of other models by breaking the dependency of system models on the results of subsystem models. Further, the advantages of multidomain models open up the doors to modeling phenomena that are extremely complex and computationally expensive to complete with available tools. New component models were developed for thin bars and plates as well as thermoviscous models of acoustic enclosures.

Competing Models of Effectiveness in Research Centers and Institutes in the Florida State Universtiy System : A Data Envelopment Analysis

이덕로 The Florida State University 2004 해외박사

This is a study about organizational effectiveness in research centers and institutes (CIs) within public higher education institutions. In particular, this study focuses on how to measure their effectiveness by integrating competing conceptions of effectiveness. This study uses a linear programming method called Data Envelopment Analysis (DEA) to examine the relative performance or organizational effectiveness of CIs based on the Competing Values Framework (CVF) as a theoretical foundation. The CVF encompasses four representative organizational effectiveness models: rational goal model, open system model, human relations model, and internal process model. Thus, in one framework, it provides the researcher with a systematically integrated way to evaluate organizational effectiveness, and affords much latitude in dealing with various different organizational contexts. Because of its utility in drawing together a variety of theoretical approaches to organizational effectiveness, the framework provides stakeholders with a balanced perspective between different organizational values. By employing DEA methodology, this study identified the “best practice” exhibited by organizations on the efficient frontier and makes recommendations regarding how sub-“best practice” CIs could become more efficient and perform according to “best practice” standards in each model of the CVF. The DEA methodology is innovative and unique in that it determines “best practice” CIs rather than the traditional comparison to "average” performance that characterizes the analytic approach most researchers currently use. This dissertation confirmed that it is better for evaluators to consider four different values, rational goal, relations with environment, human relations within the organizations, and internal work process, for a balanced judgment of effectiveness of subject organizations. In addition, a new approach combining CVF and DEA is a useful measurement tool for organizational effectiveness and a potential management tool to stimulate organizational performance. As important members of universities, CIs provide students with ample opportunities to engage their research interests. Through research activities, public service, and teaching and training by CIs, students can be trained and constituencies can be provided new knowledge-based technology and practical services. This role of the CIs is important for the future of society, and this research contributes to their effort by measuring their performance or effectiveness with a novel approach, DEA, based on the integrated theoretical foundation of organizational effectiveness, CVF. This study finds several useful results. First, the study suggests that if one wants to evaluate organizational effectiveness, using several different models is a better approach than using the traditional goal model alone to avoid misdiagnosis of the effectiveness of the organizations. Second, managers should avoid definitive effectiveness comparisons between CIs supported by different disciplines; comparisons between CIs within the same discipline are shown to be more appropriate. Third, a new approach which integrates DEA and CVF has a potential to evaluate organizational effectiveness and to be used as an organizational management tool, but other qualitative methods should be used to get additional important information about the subject organizations. University administrators and research fund providers such as federal, state, and local governments who are interested in the understanding and knowledge of student success in postsecondary education could use the results of this study to serve a variety of private and public interests.

Designing Deep Generative Models with Symbiotic Composition

Xiao, Zhisheng ProQuest Dissertations & Theses The University of 2022 해외박사(DDOD)

Generative models, especially ones that are parametrized by deep neural networks, are powerful unsupervised learning tools towards understanding complex data without label. Deep generative models have achieved tremendous success in recent years, with applications in various tasks including sample generation, image editing, visual domain adaptation, data augmentation for discriminative models and solving inverse problems. Parallel endeavors have been made along various directions – such as generative adversarial networks (GAN), variational autoencoders (VAE), normalizing flows, energy-based methods, autoregressive models, and diffusion models – and we are now able to generate increasingly photorealistic images using deep neural networks. Although these models have distinct formulations and properties, it is critical to have a clear view of fundamental deep generative models, understanding their pros and cons as well as knowing the reasons behind that. With a good understanding of existing generative learning frameworks, we can design new models that can maintain the advantages while getting rid of the limitations of previous models. Figure 1 is an illustration of the main theme of this dissertation: we give a panoramic view of the landscape of deep generative models, and design new models based on the landscape. Following the theme, the dissertation can be divided into two parts. In the first part (Chapter 1 and 2), we give a high-level overview of deep generative models and dive deep into several important models, introducing their formulations and analyzing their pros and cons carefully. Motivated by the analysis, in the second part (Chapter 3, 4, 5 and 6), we introduce four advances in the direction of designing new generative models by combining existing ones. For each new model we propose, we carefully present the formulation and explain the motivation behind the composition. We conduct comprehensive experiments to show that our proposed models can be seen as symbiotic compositions of two different generative models: the two components in each composition help each other to get rid of the limitations while keeping the advantages. It is our hope that our findings may serve as a minor contribution to the development of deep generative models.

Exploring Guidance Strategies in Diffusion-Based Generative Models

Gyuseong Lee 고려대학교 대학원 2024 국내석사

In this study, we explore diffusion models, a type of generative model known for producing highly realistic images. Specifically, we investigate two guidance strategies—Classifier Guidance (CG) and Classifier-Free Guidance (CFG)—that are vital for enhancing the quality and control of images generated by these models. Our primary contribution is a new method for conditioning diffusion models to perform examplar-based image-to-image translation. We've incorporated techniques like matching and warping into the model's sampling process, allowing it to not only generate high-quality images but also adhere to specific conditions like edge maps. Another significant aspect of our work is addressing the limitations of CFG, particularly its dependence on external conditioning factors. We introduce Self-Attention Guidance (SAG), a novel guidance approach that not only boosts image quality without external conditioning but also improves results in combination with CFG. Our findings represent a major step forward in understanding and applying conditioning techniques in diffusion models, which could significantly enhance their ability to produce high-quality images and broaden their image generation capabilities.

Tree-based models for predicting ultimate axial capacity of circular CFST columns

Nguyen, Vu Luat 세종대학교 대학원 2021 국내박사

The ultimate axial capacity (UAC) of in-filled circular concrete-filled steel tube (CCFST) columns have been the subject of considerable recent research interest because of the superior to those of either traditional reinforce concrete or conventional steel columns. The traditional (i.e., experimental, numerical, and analytical) methods in prediction of UAC of CCFST columns are still limited due to costly, time consuming, laborious, and unwell fitting external data. The demand for a new approach to predict UAC of CCFST columns is motivated by economic and engineering aspects, i.e., providing high predictive accuracy, minimizing costs and time. Tree-based models are supervised learning algorithms broadly described by stratifying or segmenting the predictor space into a number of subregions. These models provide results and tree-based structures, which help illustrate, explicitly view and interpret, and easily handle qualitative predictors. Since tree-based models have many advantages, their use in areas such as civil engineering, economics, and biostatistic has become a common alternative predictive tools for constructing classification and regression models. A single decision tree may not have the same level of predictive accuracy as some of the other machine learning-based models, so this study explores further the efficiency of ensemble trees (e.g., random forest, boosting and Bayesian approach) in predicting the UAC of CCFST columns. The procedures for optimizing hyperprameter of considered tree-based models are defined to improve predictive accuracy. At the first stage, six tree-based models (i.e., two Classification and Regression Trees models, Random Forest, Gradient Boosted Regression Tree, eXtreme Gradient Boosting, Bayesian Additive Regression Tree) are implemented using grid search and random search processes. The performance findings in terms of evaluation criterion and computational cost are recorded and compared with those obtained from available methods. The variable importance of each tree models and their structure visualizations are also investigated. Secondly, two advanced tree models, XGBoost and BART, are chosen to hybridize with three nature-inspired metaheuristic algorithms (e.g., genetic algorithm, particle swarm optimization and artificial bee colony), hereinafter called hybrid systems GAP-XGBoost and GAP-BART, respectively, in order to find the best set of hyperparameters. The efficiency of hybrid systems is found to be superior to the tree-models in the first stage and other hybrid benchmark models as well. Finally, a predictive web application is deployed for practical purposes. Thanks to this web-app, the predictability of proposed tree-based models has become more rapidly and convenient. The prediction can be performed by users who have an internet connection to their access devices without the need to install any program, even by a non-expert.

Estimating Korean Pine(Pinus koraiensis) Habitat Distribution Considering Climate Change Uncertainty : Using Species Distribution Models and RCP Scenarios = 불확실성을 고려한 잣나무의 서식 적지 분포 예측 : 종 분포 모형과 RCP시나리오를 중심으로

안윤정 서울대학교 대학원 2015 국내석사

Climate change can significantly affect tree species distribution in forests. Therefore, adaptation planning is needed to obtain maximum returns on tree growth. Pinus koraiensis, the common name is Korean pine, is a major afforestation species in Korea and is normally distributed in frigid zones. For this reason, global warming could affect the distribution of the Korean pine. Therefore, this study aimed to predict the distribution of the Korean pine and its suitable habitat area considering uncertainty by applying climate change scenarios in an ensemble model. Species distribution points and environmental variables data were used for the input data in the model. First, a site index was considered when selecting present and absent points by using the stratified method. Secondly, environmental and climate variables were chosen by literature review and then correlation analysis was performed to select variables that were not correlated. Subsequently, the selected variables were confirmed with experts. Those variables were then used as input data of BIOMOD2 (BIOdiversity MODelling 2). Next, the present distribution model was made and the result was validated with data splitting and Receiver Operating Characteristic (ROC). Next, Representative Concentration Pathways (RCPs) scenarios were applied to the models to create the future distribution model. Finally, the ensemble models were built and consensus maps were created using model committee averaging (MCA). In addition, overlay maps and uncertainty maps were used to quantify the uncertainties of the results. The estimated results of the individual models showed significant variation. Among the eight models, Random Forest (RF) had the highest accuracy. The Artificial Neural Network (ANN) model tended to overestimate results, and the Maximum Entropy Algorithm (Maxent) results were distinct from those of the other models. These differences can be explained by the algorithms of each model, the interaction of input data, and the verification methodology. The uncertain area from individual models was excluded from the ensemble model results. In the midterm future (2040s), the models themselves created the major differences observed in Korean pine distribution. In contrast, both the models and RCPs scenarios caused variation in the long-term future (2090s). Results of ensemble models were calculated using uncertainty and overlay maps, with the uncertainty of one overlay map close to 17%. The uncertainty of the five times overlaid area was around 8% in both the midterm and long-term futures. Suitable habitat for the Korean pine in the midterm future is mainly distributed in the central part of Korea, Gangwon province, and the southern part of Korea. In the long-term future, this preferred area will disappear from the southern part of Korea as well as some areas of Gangwon province. Generally, most model and ensemble results predicted that the suitable habitat area would decrease in the mid- and long-term future. As the Korean pine is an afforestation species, it cannot be planted in protected areas. Therefore, protected areas were eliminated from the results of the ensemble model. The ratios of protected area were 25%, 25%, 19%, and 22% in RCPs 2.6, 4.5, 6.0, and 8.5, respectively, in the midterm future. There was no significant difference among the results. The protected area ratios were 24%, 40%, 31%, and 24% in the long?term future, indicating that available areas to plant Korean pine will be reduced in the future. In conclusion, climate change scenarios and species distribution models (SDMs) create uncertainties in the evaluation of the future distribution of the Korean pine. Therefore, when estimating species distribution under climate change, uncertainties should be considered. In addition, the models show that the suitable habitat area for the Korean pine will decrease in the future, making it important for the climate change adaptation plan to reduce this impact. This study is significant in that it considered uncertainties in the SDMs and RCPs scenarios. The results of this study could be important considerations in the process of plantation planning.

Improving Fine-tuning of Language Models with an Emphasis on Isotropy and Rank

정은아 서울대학교 대학원 2024 국내박사

In the field of natural language processing, a strategy to fine-tune pre-trained language models for downstream tasks is a fundamental approach. Among many fine-tuning tasks, learning text embedding (representation) that captures the underlying semantic information of a given text is an essential task. Given the remarkable progress in the linguistic comprehension capabilities of large-scale pre-trained language models (PLMs), there has been a significant surge in the development of text embedding models leveraging these PLMs in recent times. This dissertation focuses on representations of language models such as Bidirectional Encoder Representations from Transformers (BERT) and studies the techniques to improve the performance of text embeddings. We delve into the two text embedding tasks, Dense Retrieval (DR) and sentence embedding. In the realm of information retrieval, DR models encode queries and documents, thereby generating representations for queries and documents. Using these representations, the relevance between the query and the document is determined. However, representations of PLMs are known to follow an anisotropic distribution, which can be undesirable for relevance estimation. We reveal that representations of popular BERT-based DR models such as ColBERT and RepBERT follow an anisotropic distribution. To cope with the problem, we adopt unsupervised post-processing methods of Normalizing Flow and whitening, which can effectively enhance the isotropy of representations, thereby improving the performance of DR models. Furthermore, with post-processing methods, we can significantly improve the performance of DR models for the out-of-distribution tasks where the distribution of the test dataset differs from that of the training dataset. The next task we focus on is the sentence embedding task. Sentence embedding models estimate the semantic similarity between two given sentences by measuring the similarity between two sentences’ representations. Unsupervised learning of sentence embedding aims to learn representations that capture the underlying semantic information of sentences without the need for human annotation. Among numerous unsupervised models for the sentence embedding task, SimCSE has made a significant progress through self-supervised contrastive learning and has become a foundational baseline for subsequent studies in the field. In pursuit of improving sentence embedding performance through self-supervised learning (SSL), we focus on the representations of SimCSE. Through an in-depth exploration of SimCSE's training dynamics, we uncover a strong correlation between representation rank and performance. Building upon this insight, we introduce the Rank Reduction (RR) regularizer to the fine-tuning of SimCSE. Our experiments reveal that RR not only boosts the performance of SimCSE in sentence embedding tasks but also contributes to the model's stability against changes in random seeds. This result offers valuable insights into the relationship between representation rank and SSL performance in natural language processing, potentially benefiting a wide range of applications.

An Analysis of UK Urban Fringe Management Models Applying the Actor Network Theory

김용범 Cardiff University 2002 해외박사

The main objectives of this research are to investigate and analyse the operation processes of urban fringe management models using the actor network theory concept, especially the sociology of translation. The perceptions, actions and reactions of local interest groups and agencies to local circumstances produced a complex framework from which particular management models emerged. Imporlantly, because of their limited statutory and financial powers, local authorities were unable to effectively and efficiently deal with localities as a result of the deregulation of property rights of a variety of landownerships. This inability crucially led to the formation of the management models with the anticipation of building a coalition of interest groups and public and non-government organisations in the management processes in order to improve the physical, economic and social environments and facilitate the management mechanism. However, the instigators of the management models did not seek to make themselves indispensable, rather they sought to make their proposals, the management models, indispensable as instruments for constructing actor networks, sustaining and enhancing the environment and managing the urban fringe. The management models' objectives were to gain and maintain the interests of other actors and to ensure their enrolment in their actor networks. Accordingly, the operational processes of the management models were dependent on capturing the interests of other actors. In addition, although the role of speaking on behalf of etch management model's actor network is differently invested in certain internal organisation bodies, such as the governance body and management body, what is common to all the urban fringe management models is that the governance body within each management model tries to speak with one voice to bring about an effective operation process, and the management team employs a variety of devices to secure the enrolment of a variety of actors in its actor network.