959 research outputs found
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 sectorâs 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 âmissionâ
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 âŹ130m 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 Countyâs 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
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