Enabling object storage via shims for grid middleware

The Object Store model has quickly become the basis of most commercially successful mass storage infrastructure, backing so-called "Cloud" storage such as Amazon S3, but also underlying the implementation of most parallel distributed storage systems. Many of the assumptions in Object Store design are similar, but not identical, to concepts in the design of Grid Storage Elements, although the requirement for "POSIX-like" filesystem structures on top of SEs makes the disjunction seem larger. As modern Object Stores provide many features that most Grid SEs do not (block level striping, parallel access, automatic file repair, etc.), it is of interest to see how easily we can provide interfaces to typical Object Stores via plugins and shims for Grid tools, and how well experiments can adapt their data models to them. We present evaluation of, and first-deployment experiences with, (for example) Xrootd-Ceph interfaces for direct object-store access, as part of an initiative within GridPP[1] hosted at RAL. Additionally, we discuss the tradeoffs and experience of developing plugins for the currently-popular Ceph parallel distributed filesystem for the GFAL2 access layer, at Glasgow

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others. Still, most approaches typically address visual tasks in isolation, resulting in overspecialized models which achieve strong performances in specific applications but work poorly in other (often related) tasks. This is clearly sub-optimal for a robot which is often required to perform simultaneously multiple visual recognition tasks in order to properly act and interact with the environment. This problem is exacerbated by the limited computational and memory resources typically available onboard to a robotic platform. The problem of learning flexible models which can handle multiple tasks in a lightweight manner has recently gained attention in the computer vision community and benchmarks supporting this research have been proposed. In this work we study this problem in the robot vision context, proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art algorithms in this novel challenging scenario. We also define a new evaluation protocol, better suited to the robot vision setting. Results shed light on the strengths and weaknesses of existing approaches and on open issues, suggesting directions for future research.Comment: This work has been submitted to IROS/RAL 201

Designing and Implementing a Learning Object Repository: Issues of Complexity, Granularity, and User Sense-Making

4th International Conference on Open RepositoriesThis presentation was part of the session : DSpace User Group PresentationsDate: 2009-05-20 03:30 PM – 05:00 PMThe Texas Center for Digital Knowledge at the University of North Texas is designing and implementing a DSpace/Manakin learning object repository (LOR) for the Texas Higher Education Coordinating Board to store and provide access to redesigned undergraduate courses being created through the Board's Texas Course Redesign Project (TCRP). The content for the THECB LOR differs in significant ways from content stored in other well-known and evolving LORs, since the content is in the form of complete or partial courses. While this content can be represented as a single learning object (i.e., a complete course as one learning object), the THECB LOR is making the complete courses available as learning objects and it is providing access to components of the courses' content as discrete learning objects for reuse and repurposing. A number of challenges and issues have emerged in the design, development, and implementation the LOR, and this paper focuses on three key aspects and the solutions we are pursuing: 1) complexity of the course content and granularity; 2) submission of complex objects and metadata; and 3) user interface design to assist users in making sense of this repository and its contents.Texas Higher Education Coordinating Boar