A church and family housing for Berkland Baptist Church (BBC)

Thesis (M.Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 2001.Includes bibliographical references (p. 104-107).Creating a sound church is totally different from designing a fabulous poetic space. Major problem of current built form of a church is that it is built mostly in a liturgical form to serve sacred ordinances that does not address the importance of activities among the members. Church has turned into a liturgical space only to serve once-a-week spiritual purgation. This causes serious problems to Christians. There is a big dichotomy between their actual life and religious life. It is a constant struggle for Christians to figure out on what values -Christian or Daily-to make decision to perform their life. A church is a body of Christ where one not only finds the eternal life by faith, but also gathers to lead a life based on Christian values. Therefore, a church has to be a part of actual living. Berkland Baptist Church (BBC) is one of the leading churches that address to return to the spirit of early churches where religious life and daily life are fully integrated. This thesis, thus, explores a new concept of what a built form of a church would be. The final product has informed that a church is not a single building with well contrived light to arouse spiritual excitement, but an assemblage of functions - church & housing - that invigorate communal activities among the faithful.Joong Won Lee.M.Arch

Exploring Gender Differences on Generation Y’s Attitudes towards Green Practices in a Hotel

One of the early green measures, the Save the Earth campaign, has offered significant bottom-line savings for hotels since 1993. In the current economic recession, new green measures for emerging group of customers could offer another way of saving operating cost and conserving local environment for the hotel. As environmentally conscious Gen Yers become a frequent traveler segment, identifying green attributes they are willing to accept becomes vital. Thus, this study examines the attitudes of Gen Yers toward green practices and identifies green attributes that Gen Y males and females prefer. The results show significant differences in the attributes that each gender prefers, and offer segmentation guidelines and suggest specific environmental friendly services that would result in cost saving for Gen Yers. According to the results of a proposed model, hotel companies should make more efforts to promote their green practices to Gen Y hotel guests who are willing to pay more for them

Effects of Culture and Service Quality on Affective Service Experience Quality of Guests

This study examined the effect of culture and dimensions of service quality on positive affect, negative affect and satisfaction of hotel guests following a service encounter. Each of 82 participants viewed eight video clips of staged service encounters. Video clips ranged from 5-8 minutes in duration. Based on an orthogonal design, each video depicted a unique combination of levels of five service quality dimensions: reliability, responsiveness, empathy, tangibles, and assurance (Parasuraman, Zeithaml, and Berry 1988). Following each clip, participants completed self-report measures of affect and satisfaction. Data were analyzed using hierarchical linear modeling techniques (Raudenbush and Bryk 2002; Luke, 2004). The presence or absence of each service quality dimension in the model was indicated with dummy vectors. Results indicate that service experience of guests is substantially affected by the five service quality dimensions, but, in the population included in the experiment, those dimensions do not interact with culture. This study suggests that service providers might optimize guest experiences by focusing on preparation of staff to meet empathy and assurance needs of guests, in addition to the other service quality dimensions

Localization Uncertainty Estimation for Anchor-Free Object Detection

Since many safety-critical systems, such as surgical robots and autonomous driving cars, are in unstable environments with sensor noise and incomplete data, it is desirable for object detectors to take into account the confidence of localization prediction. There are three limitations of the prior uncertainty estimation methods for anchor-based object detection. 1) They model the uncertainty based on object properties having different characteristics, such as location (center point) and scale (width, height). 2) they model a box offset and ground-truth as Gaussian distribution and Dirac delta distribution, which leads to the model misspecification problem. Because the Dirac delta distribution is not exactly represented as Gaussian, i.e., for any $\mu$ and $\Sigma$. 3) Since anchor-based methods are sensitive to hyper-parameters of anchor, the localization uncertainty modeling is also sensitive to these parameters. Therefore, we propose a new localization uncertainty estimation method called Gaussian-FCOS for anchor-free object detection. Our method captures the uncertainty based on four directions of box offsets~(left, right, top, bottom) that have similar properties, which enables to capture which direction is uncertain and provide a quantitative value in range~[0, 1]. To this end, we design a new uncertainty loss, negative power log-likelihood loss, to measure uncertainty by weighting IoU to the likelihood loss, which alleviates the model misspecification problem. Experiments on COCO datasets demonstrate that our Gaussian-FCOS reduces false positives and finds more missing-objects by mitigating over-confidence scores with the estimated uncertainty. We hope Gaussian-FCOS serves as a crucial component for the reliability-required task

Wasserstein Geodesic Generator for Conditional Distributions

Generating samples given a specific label requires estimating conditional distributions. We derive a tractable upper bound of the Wasserstein distance between conditional distributions to lay the theoretical groundwork to learn conditional distributions. Based on this result, we propose a novel conditional generation algorithm where conditional distributions are fully characterized by a metric space defined by a statistical distance. We employ optimal transport theory to propose the \textit{Wasserstein geodesic generator}, a new conditional generator that learns the Wasserstein geodesic. The proposed method learns both conditional distributions for observed domains and optimal transport maps between them. The conditional distributions given unobserved intermediate domains are on the Wasserstein geodesic between conditional distributions given two observed domain labels. Experiments on face images with light conditions as domain labels demonstrate the efficacy of the proposed method