Library management systems in the UK: 1960s-1980s

Tedd, L.A. (2007). Library management systems in the UK: 1960s-1980s. Library History, 23(4),301-316 Originally published (as above) by Maney Publishing.

MUES (Mid Wales - Users - Ethnic Services) Ethnic services provision 2007-08. Report for Libraries for Life: Delivering the entitlement agenda for library users in Wales 2007-09

Thomas, R., Urquhart, C., Crossan, S. & Hines, B. (2008). MUES (Mid Wales - Users - Ethnic Services) Ethnic services provision 2007-08. Report for Libraries for Life: Delivering the entitlement agenda for library users in Wales 2007-09. Aberystwyth: Department of Information Studies, Aberystwyth University. Related policy guidance published separately Sponsorship: CyMAL

MUES (Mid Wales - Users - Ethnic Services) Ethnic services provision 2007-08. Policy guidance for Libraries for Life: Delivering the entitlement agenda for library users in Wales 2007-09

Urquhart, C., Thomas, R., Crossan, S. & Hines, B. (2008). MUES (Mid Wales - Users - Ethnic Services) Ethnic services provision 2007-08. Policy guidance for Libraries for Life: Delivering the entitlement agenda for library users in Wales 2007-09. Aberystwyth: Department of Information Studies, Aberystwyth University. Relates to report of same title - http://hdl.handle.net/2160/609 Sponsorship: CyMAL

Rural information needs. Final report for Mid Wales Library and Information Partnership

Thomas, R., Crossan, S., Urquhart, C. & Hines, B. (2008). Rural information needs. Final report for Mid Wales Library and Information Partnership. Aberystwyth: Department of Information Studies, Aberystwyth University Sponsorship: Mid Wales Library and Information Partnership

Assessing the impact of a health library service. Best practice guidance

Urquhart, C. & Weightman, A. (2008). Assessing the impact of a health library service. Best Practice Guidance. Based on research originally funded by LKDN, now sponsored by National Library for Health. Aberystwyth: Department of Information Studies, Aberystwyth University. The guidance relates to a project report, Developing a toolkit for assessing the impact of health library services on patient care (also available in CADAIR). A version of this item is available as an online appendix to a paper in Health Information and Libraries Journal entitled: The value and impact of information provided through library services for patient care: developing guidance for best practice (Weightman, A., Urquhart, C. et al) available electronically prepublication Sponsorship: LKDN/NLH

Library management systems

Tedd, L.A. (2007). Library management systems. In J.H. Bowman (Ed.), British librarianship and information work 2001-2005 (pp.431-453). Aldershot:Ashgate.

Library management systems

Tedd, L.A. (2006). Library management systems. In J. H. Bowman(Ed.), British Librarianship and Information Work 1991-2000 (pp.452-471). Aldershot:Ashgate

Construction of a Festuca pratensis BAC library for map-based cloning in IFestulolium substitution lines

Iain S. Donnison, Donal M. O Sullivan, Ann Thomas, Peter Canter, Beverley Moore, Ian Armstead, Howard Thomas, Keith J. Edwards and Ian P. King (2005). Construction of a Festuca pratensis BAC library for map-based cloning in Festulolium substitution lines. Theoretical and Applied Genetics, 110 (5) pp.846-851 Sponsorship: BBSRC;BBSRC RAE2008

Library as Agent of [Re]Contextualization

Paper presented at the Digital Humanities 2009 conference in College Park, Maryland.

Cloud-sourcing Research Collections: Managing Print in the Mass-digitized Library Environment

A report of key findings of the Cloud Library project, an effort jointly designed and executed by OCLC Research, the HathiTrust, New York University's Elmer Bobst Library, and the Research Collections Access & Preservation (ReCAP) consortium, with support from the The Andrew W. Mellon Foundation. The objective of the project was to examine the feasibility of outsourcing management of low-use print books held in academic libraries to shared service providers, including large-scale print and digital repositories.

Collection Development and the Value of the Library

This is a draft 2 of a discussion paper written for Boston University Libraries

Library Technology Services at Boston University: an un-roadmap

A working paper for discussion

The Specialized Mappings Architecture

A probabilistic, nonlinear supervised learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA employs a set of several forward mapping functions that are estimated automatically from training data. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). The SMA can model ambiguous, one-to-many mappings that may yield multiple valid output hypotheses. Once learned, the mapping functions generate a set of output hypotheses for a given input via a statistical inference procedure. The SMA inference procedure incorporates an inverse mapping or feedback function in evaluating the likelihood of each of the hypothesis. Possible feedback functions include computer graphics rendering routines that can generate images for given hypotheses. The SMA employs a variant of the Expectation-Maximization algorithm for simultaneous learning of the specialized domains along with the mapping functions, and approximate strategies for inference. The framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human’s body or hands, given silhouettes from a single image. The accuracy and stability of the SMA are also tested using synthetic images of human bodies and hands, where ground truth is known.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.