CAD-model-based vision for space applications

A pose acquisition system operating in space must be able to perform well in a variety of different applications including automated guidance and inspections tasks with many different, but known objects. Since the space station is being designed with automation in mind, there will be CAD models of all the objects, including the station itself. The construction of vision models and procedures directly from the CAD models is the goal of this project. The system that is being designed and implementing must convert CAD models to vision models, predict visible features from a given view point from the vision models, construct view classes representing views of the objects, and use the view class model thus derived to rapidly determine the pose of the object from single images and/or stereo pairs

An Intuitive Graphical Query Interface for Protégé Knowledge Bases

Emily is a graphical query engine for Protégé knowledge bases that was developed by the Structural Informatics Group (SIG) at the University of Washington. Currently this application is adapted for a specific knowledge model, the Foundational Model of Anatomy (FMA) [1], but it could readily be generalized for use with other Protégé knowledge bases. In developing the Emily query interface, our intent was to provide a tool that was simple and intuitive to use, like the Queries tab provided with Protégé, but with improved information retrieval capabilities. Although some more advanced query mechanisms exist, currently they are too complicated for non-expert end users. The Algernon tab [2], for example, provides extensive Protégé query capabilities but requires users to learn a query scripting language. We sought to develop a query interface that was intuitive enough for end users to operate, with only minor instruction, yet was powerful enough to gather interesting information from a knowledge base that was not easily attained by browsing alone

Women who have a graduate school education who have chosen to make mothering the major focus of their time : a descriptive study

This is a descriptive study of 33 graduate school educated women who have chosen to stay home with their children. The study includes a discussion of five major areas: personality characteristics, decision-making process, level of job satisfaction, peer and family relationships, and self-image.;The Methodology for this study involved four data gathering procedures: the demographic data form, the structural interview, the California Psychological Inventory (CPI), and the Bems Sex-Role Inventory (BSRI). Participants were obtained by sending fliers home with children at five preschools in the Richmond, Virginia Metropolitan area.;Demographic Data. The mean age for the group was 34. All were part of an intact two parent family. Fourteen graduate majors were represented. Ninety percent had held jobs which were directly related to their advanced degree. Seventy-six percent are actively involved in a career-related activity.;Personality Characteristics. This high functioning group has a composite profile that shows the ability to achieve independently and they prefer their own judgement. They have strength intellectually. The composite personality is someone who has successfully combined some of the best parts of traditional masculine and feminine qualities.;Decision-making Process. The reasons for choosing to stay home related to feeling that their family was their main priority and they didn\u27t want someone else raising their children.;Job Satisfaction. Neither level of status nor dissatisfaction with their last job was the primary reason for choosing to stay home at the time.;Peer and Family Relationships. Support systems were extremely important to this group. Husbands were also very involved in decision-making, child care, and emotional support.;Self-image. These women generally feel good about their choices and believe this is the right role for them at this time. They are aware of what they have given up, but believe they and their children have gained much more than they could ever give up

Learning to Segment Breast Biopsy Whole Slide Images

We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks cannot be applied directly on large biopsy images and the different sized structures in biopsies present novel challenges, we propose four modifications: (1) an input-aware encoding block to compensate for information loss, (2) a new dense connection pattern between encoder and decoder, (3) dense and sparse decoders to combine multi-level features, (4) a multi-resolution network that fuses the results of encoder-decoders run on different resolutions. Our model outperforms a feature-based approach and conventional encoder-decoders from the literature. We use semantic segmentations produced with our model in an automated diagnosis task and obtain higher accuracies than a baseline approach that employs an SVM for feature-based segmentation, both using the same segmentation-based diagnostic features.Comment: Added more WSI images in appendi

Does Involvement in Religion Help Prisoners Adjust to Prison? (FOCUS)

Research conducted by the National Council on Crime and Delinquency has uncovered an abundant variety of religious responses to incarceration. First, religious participation can help an inmate overcome the depression, guilt, and self-contempt that so often accompanies the prison sentence. Second, inmates may seek a way to avoid the constant threats faced in prison. In many ways, the prisoner's desire for religion is not very different from that of the free-world citizen in that he or she seeks religion to make life more livable

An Ontology-based Image Repository for a Biomedical Research Lab

We have developed a prototype web-based database for managing images acquired during experiments in a biomedical research lab studying the factors controlling cataract development. Based on an evolving ontology we are developing for describing the experimental data and protocols used in the lab, the image repository allows lab members to organize image data by multiple attributes. The use of an ontology for developing this and other tools will facilitate intercommunication among tools, and eventual data sharing with other researchers