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.

A multi-perspective approach to performance anxiety in music students

AbstractPerforming publicly has become increasingly important in a variety of professions. This condition is associated with performance anxiety in almost all performers. Whereas some performers successfully cope with this anxiety, for others it represents a major problem and even threatens their career. Musicians and especially music students were shown to be particularly affected by performance anxiety.Therefore, the goal of this PhD thesis was to gain a better understanding of performance anxiety in university music students. More precisely, the first part of this thesis aimed at increasing knowledge on the occurrence, the experience, and the management of performance anxiety (Article 1). The second part aimed at investigating the hypothesis that there is an underlying hyperventilation problem in musicians with a high level of anxiety before a performance. This hypothesis was addressed in two ways: firstly, by investigating the association between the negative affective dimension of music performance anxiety (MPA) and self-perceived physiological symptoms that are known to co-occur with hyperventilation (Article 2) and secondly, by analyzing this association on the physiological level before a private (audience-free) and a public performance (Article 3). Article 4 places some key variables of Article 3 in a larger context by jointly analyzing the phases before, during, and after performing.The main results of the self-report data show (a) that stage fright is experienced as a problem by one-third of the surveyed students, (b) that the students express a considerable need for more help to better cope with it, and (c) that there is a positive association between negative feelings of MPA and the self-reported hyperventilation complaints before performing. This latter finding was confirmed on the physiological level in a tendency of particularly high performance-anxious musicians to hyperventilate. Furthermore, the psycho-physiological activation increased from a private to a public performance, and was higher during the performances than before or after them. The physiological activation was mainly independent of the MPA score. Finally, there was a low response coherence between the actual physiological activation and the self-reports on the instantaneous anxiety, tension, and perceived physiological activation.Given the high proportion of music students who consider stage fright as a problem and given the need for more help to better cope with it, a better understanding of this phenomenon and its inclusion in the educational process is fundamental to prevent future occupational problems. On the physiological level, breathing exercises might be a good means to decrease - but also to increase - the arousal associated with a public performance in order to meet an optimal level of arousal needed for a good performance.

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.

How the mind constitutes itself through perceptual learning

Most theories of perception assume a rigid relationship between objects of the physical world and the corresponding mental representations. We show by a priori reasoning that this assumption is not fulfilled. We claim instead that all object-representation correspondences have to be learned. However, we cannot learn to perceive all objects that there are in the world. We arrive at these conclusions by a combinatory analysis of a fictive stimulus world and the way to cope with its complexity, which is perceptual learning. We show that successful perceptual learning requires changes in the representational states of the brain that are not derived directly from the constitution of the physical world. The mind constitutes itself through perceptual learning.