Social justice on the margins: the future of the not for profit sector as providers of legal advice in England and Wales

The Legal Aid, Sentencing and Punishment of Offenders Act (LASPO) has been described by many commentators as a dramatic curtailment of access to justice which is likely to impact disproportionately on marginalised groups and individuals. This paper seeks to set LASPO in its historical context - as a radical development, but nevertheless one that is consistent with the policy discourses of responsibilization and consumerism dominant from the 1990s. It uses research into the experience of the Not For Profit sector’s involvement in legally aided welfare advice to frame this perspective. Key findings include the extent to which respondents (both managers and front line workers) felt that Legal Services Commission funding had transformed organizational practices and ethos but that the implementation of LASPO and the austerity programme represented a critical watershed for the sector and its capacity to fulfil what front line workers in particular felt was their ‘mission’

Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy

Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification

We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets.Comment: 5 pages, three figure

Competence and quality in the training of teachers for the post compulsory sector in the UK

This issue addresses the broad theme of quality learning. When we invited authors to submit papers for this special issue. we were not prescriptive about what we meant by quality learning However. we were especially interested in some aspects of learning that are particularly important to teacher education at the present time. One of these relates to the quality of the learning outcomes that are achieved by teacher education students. What sort of outcomes should prospective teachers achieve before they take on the complex task of helping others learn

Young people's resilience and involvement : possible elements of the European Union's Structural and Investment Funds in addressing youth unemployment?

This paper explores the role of the EU's Structural and Investment Funds (ESIF) in addressing youth unemployment. This paper looks beyond the now well established repertoire of ESIF interventions. It considers evidence on two possible areas for intervention: the involvement of young people in the design and delivery of programmes, and the development of young people's personal resilience as a determinant of successful labour market outcomes. Findings are presented from a large scale evaluation of a €130m seven year programme (called Talent Match) in England which is being funded by the United Kingdom's Big Lottery Fund (the main distributor of Lottery funding in the UK). It outlines the opportunities and constraints from both involvement and resilience approaches, and how at first sight, the two approaches appear to stem for quite different conceptions of the determinants of youth unemployment. In conclusion, it suggests how by using Sen's capabilities approach, youth involvement and personal resilience may be reconciled and the possible response for the ESIF

Visioning the Allen Creek Greenway: Designing a Path, Creating a Place

This report describes background, analysis, layout and design for the Allen Creek Greenway in Ann Arbor, Michigan. The authors define the greenway land use form as a linear park which fits within a large network of regional green infrastructure; examine the history of greenways and their strong public appeal; and describe the significant ecological, social, and economic benefits which the Allen Creek Greenway could bring to Ann Arbor. The report describes the preliminary layout and design for the Allen Creek Greenway along the Ann Arbor Railroad as well as conceptual open space designs for three city-owned parcels that occur along its length: the parcels at First St. and William St., 415 W. Washington St., and 721 N. Main St. GIS software was used to analyze existing site conditions so that the designs take into account the full complexity of the context including current land use, topography, and water movement. The proposed route is almost entirely within the Ann Arbor Railroad ROW, running from just south of the University of Michigan stadium to the Huron River, where it will connect to Washtenaw County’s Border to Border trail, giving residents better access to regional greenspace. The greenway approximately follows the historic path of Allen Creek; the creek is now buried in a pipe. Because of this, most of the greenway is within the floodplain and a significant portion is within the floodway of the creek. There are federal restrictions on development within this designated flood area and thus the greenway is ideal because it is one of the few permitted uses. Because of the complexity of the greenway project, this report details a phased implementation plan, beginning with the creation of designated on-street routes. The Allen Creek Greenway, mentioned by name increasingly in city plans, has the capacity to serve as an anchor and a green amenity to the downtown core and provide a catalyst for economic and sustainable development in the surrounding area along its entire length.Master of Landscape ArchitectureNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/90880/1/AllenCreekGreenwayPracticumFinalReport.pd

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126