The letters of Charlotte Mary Yonge (1823-1901) edited by Charlotte Mitchell, Ellen Jordan and Helen Schinske.

Charlotte Yonge is one of the most influential and important of Victorian women writers; but study of her work has been handicapped by a tendency to patronise both her and her writing, by the vast number of her publications and by a shortage of information about her professional career. Scholars have had to depend mainly on the work of her first biographer, a loyal disciple, a situation which has long been felt to be unsatisfactory. We hope that this edition of her correspondence will provide for the first time a substantial foundation of facts for the study of her fiction, her historical and educational writing and her journalism, and help to illuminate her biography and also her significance in the cultural and religious history of the Victorian age

The Elevator Only Goes Up

Dimensions: 30 inches wide, 36 inches tall Inkjet on matte paper, printed on both sides Artist\u27s narrative: This letter takes Paul Laurence Dunbar back to when he was not proud of his work. In high school, he thought all of his writing was one big joke. He could not afford much, which led to him being forced to take a job as an elevator hopper. In the end, it was a situation that should be seen as a positive. When talking in the elevator with people, he was able to learn how they spoke and put it into his writing. When he finally finished Oak and Ivy, he began to sell his work within the elevator, allowing for additional opportunities to promote his work. Font palette: FreightNeo Pro, designed by Robby Woodard; AF Lapture, designed by Tim Ahrens These type families captured what felt like a memory that went much deeper than what was on the outside. Lapture is an old face that represents the flashback of the memory. FreightNeo Pro captures the beauty behind everything Paul Laurence Dunbar has done. The imagery represents his journey up and down the elevator, capturing the “footsteps” through the first parts of his life. The red throughout the piece represents the bad memories of high school and what brought him to the elevator. The elevator symbolizes the starting point of Dunbar’s career—he could only go up from here.https://ecommons.udayton.edu/stu_vad_dunbarletters/1023/thumbnail.jp

Gaussian Processes with Context-Supported Priors for Active Object Localization

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. In experiments, we compare our approach to a state-of-theart bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure

Semantic Image Retrieval via Active Grounding of Visual Situations

We describe a novel architecture for semantic image retrieval---in particular, retrieval of instances of visual situations. Visual situations are concepts such as "a boxing match," "walking the dog," "a crowd waiting for a bus," or "a game of ping-pong," whose instantiations in images are linked more by their common spatial and semantic structure than by low-level visual similarity. Given a query situation description, our architecture---called Situate---learns models capturing the visual features of expected objects as well the expected spatial configuration of relationships among objects. Given a new image, Situate uses these models in an attempt to ground (i.e., to create a bounding box locating) each expected component of the situation in the image via an active search procedure. Situate uses the resulting grounding to compute a score indicating the degree to which the new image is judged to contain an instance of the situation. Such scores can be used to rank images in a collection as part of a retrieval system. In the preliminary study described here, we demonstrate the promise of this system by comparing Situate's performance with that of two baseline methods, as well as with a related semantic image-retrieval system based on "scene graphs.