Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

PHA urges parents to get 'at risk' children vaccinated against seasonal flu

The Public Health Agency is urging Northern Ireland parents to make sure children in 'at risk' groups get their flu vaccine early.The message has been issued to parents and carers of children as the PHA's seasonal flu vaccination programme gets underway for 2011/12.It is very important that children with any condition that puts them more at risk of the complications of flu get the vaccine.These 'at risk' conditions include:chronic lung conditions such as asthma;chest infections that have required hospital admission;chronic heart conditions;chronic liver disease;chronic kidney disease;diabetes;lowered immunity due to disease or treatment such as steroids or cancer therapy;chronic neurological conditions such as stroke, multiple sclerosis or a condition that affects the nervous system, such as cerebral palsy;hereditary and degenerative diseases of the central nervous system or muscles.Children who attend special schools for severe learning or physical disabilities are considered to be particularly at risk, as well as those with other complex health needs.The PHA has written to principals of local special schools, as well as parents of children at these schools, to raise awareness of the importance of getting vaccinated early.Dr Richard Smithson, PHA Flu Vaccination Lead, said: "For many people, flu is a short, unpleasant illness, but it does not usually cause any serious problems. However, for others, it can have very serious complications including, in rare cases, being fatal."We have been particularly reminded over the last two winters that children with chronic neurological problems and other complex health needs are very vulnerable to these complications. We have seen children become very seriously ill and, tragically, there have even been a few deaths in children who attend special schools."For this reason, we recommend that all children who attend special schools for severe learning disability, and special schools for physical disability, are offered the flu vaccine early in the autumn, before the flu viruses start circulating."The vaccine is now available from GP surgeries and the PHA recommends that parents check arrangements with their own GP's surgery so that their child can get the jab.The earlier you get vaccinated the better, as it takes the body about 10-14 days after the jab to develop antibodies. These will then protect you against the same or similar viruses if the body is exposed to them. The vaccine contains three strains of the flu virus, which are considered the most likely to be circulating this winter, including the H1N1 (swine flu) virus."Your child needs to get the flu jab every year - the protection it gives only lasts for one winter, so even if they got it last year, they still need to get it this year," added Dr Smithson."Also, if your child has been diagnosed with flu or swine flu in the past couple of years, they will still need the jab this year as there are different types of flu that the jab will protect against. Getting the flu jab is the best way to protect your child against flu and we would strongly recommend that you arrange for them to have it."Although the vaccine gives good protection, no vaccine gives total protection, so if your child develops flu-like symptoms (such as fever, cough, aches and pains, and sore throat) you should contact your GP for advice. If your child has any of these symptoms, they should be kept at home until they feel better."For more information on seasonal flu, go to www.fluawareni.info and follow us on Facebook and Twitter.

Adult Learning Services

Evidence Review 4 - Adult learning services Briefing 4 - Adult learning services This pair of documents, commissioned by Public Health England, and written by the UCL Institute of Health Equity, address the role of participation in learning as an adult in improving health. There is evidence that involvement in adult learning has both direct and indirect links with health, for example because it increases employability. There is some evidence that those who are lower down the social gradient benefit most, in health terms, from adult learning. However, there is a gradient both in participation in adult learning and skill level, whereby the more someone would benefit from adult learning, the less likely they are to participate, and the lower their literacy and numeracy skills are likely to be. This is due to a range of barriers, including prohibitively high costs, lack of personal confidence, or lack of availability and access. These papers also show that there are a number of actions local authorities can take to increase access to adult learning, improve quality of provision and increase the extent to which it is delivered and targeted proportionate to need. The full evidence review and a shorter summary briefing are available to download above. This document is part of a series. An overview document which provides an introduction to this and other documents in the series, and links to the other topic areas, is available on the ‘Local Action on health inequalities’ project page. A video of Michael Marmot introducing the work is also available on our videos page.

Learning collaboration in General Internal Medicine: a pilot project for undergraduate students in nursing, physiotherapy and medicine

Background: A form of education called Interprofessional Education (IPE) occurs when two or more professions learn with, from and about each other. The purpose of IPE is to improve collaboration and the quality of care. Today, IPE is considered as a key educational approach for students in the health professions. IPE is highly effective when delivered in active patient care, such as in clinical placements. General internal medicine (GIM) is a core discipline where hospital-based clinical placements are mandatory for students in many health professions. However, few interprofessional (IP) clinical placements in GIM have been implemented. We designed such a placement. Placement design: The placement took place in the Department of Internal Medicine at the CHUV. It involved students from nursing, physiotherapy and medicine. The students were in their last year before graduation. Students formed teams consisting of one student from each profession. Each team worked in the same unit and had to take care of the same patient. The placement lasted three weeks. It included formal IP sessions, the most important being facilitated discussions or "briefings" (3x/w) during which the students discussed patient care and management. Four teams of students eventually took part in this project. Method: We performed a type of evaluation research called formative evaluation. This aimed at (1) understanding the educational experience and (2) assessing the impact of the placement on student learning. We collected quantitative data with pre-post clerkship questionnaires. We also collected qualitative data with two Focus Groups (FG) discussions at the end of the placement. The FG were audiotaped and transcribed. A thematic analysis was then performed. Results: We focused on the qualitative data, since the quantitative data lacked of statistical power due to the small numbers of students (N = 11). Five themes emerged from the FG analysis: (1) Learning of others' roles, (2) Learning collaborative competences, (3) Striking a balance between acquiring one's own professional competences and interprofessional competences, (4) Barriers to apply learnt IP competences in the future and (5) Advantages and disadvantages of IP briefings. Conclusions: Our IP clinical placement in GIM appeared to help students learn other professionals' roles and collaborative skills. Some challenges (e.g. finding the same patient for each team) were identified and will require adjustments.

EU report on the implementation of the UN Convention on the Rights of Persons with Disabilities

On 5 June 2014, the European Union published its first report on the implementation of the UN Convention on the Rights of Persons with Disabilities (UN CRPD). This document follows the ratification of the Convention by the EU in 2010 and its obligation to prepare and submit a report on its actions to support the implementation of the Convention. Read the report here.

The Healthier Communities Shared Priority Project: Learning from the pathfinder authorities

This report presents the findings of an evaluation of how the 12 pathfinder local authorities in the LGA/DH sponsored Shared Priority Project began engaging with new requirements to promote healthier communities and narrow health inequalities. The purpose of the report is to capture the learning from the pathfinder authorities' experience of this initial planning phase and share it more widely now that all local authorities have to focus on the shared priorities.

Health equity audit - learning from practice briefing

This briefing provides a summary of learning from three workshops on HEA, and examples of completed or near-completed HEAs to illustrate these learning points. It is recognised that this experience is evolving.

E-learning : implementació d'un CMS per a la gestió d'un Campus Virtual

L'objectiu d'aquest treball consisteix a elegir una eina de gestió de continguts per a l'aplicació en la gestió d'un campus virtual. L'estudi de les característiques de diversos dels CMS més emprats ens permetrà, a més, acostar-nos-hi d'una manera pràctica, atès que una part del treball inclou, després de la tria del CMS, la seva instal·lació en un servidor a Internet i la configuració pertinent per a adequar-lo als rols d'administradors, professors, estudiants, etc.

Communities for health: Learning from the pilots

Learning case examples and best practice from the pilot areas of Communities for Health. These pilots detail how communities have addressed a wide range of health issues and tackled health inequalities. Rural and urban deprived areas have worked to address obesity, healthy eating, mental health and sexual health.

TFC e-learning estàndards : Anàlisi de la qualitat de les metadades presents als documents LOM

Aquest projecte vol estudiar la qualitat de les metadades presents en els documents que representen objectes de coneixement LOM (