Supporting interdisciplinary case studies: development and implementation of a joint learning environment for students in forensic science and criminal law

Scientific reporting and communication is a challenging topic for which traditional study programs do not offer structured learning activities on a regular basis. This paper reports on the development and implementation of a web application and associated learning activities that intend to raise the awareness of reporting and communication issues among students in forensic science and law. The project covers interdisciplinary case studies based on a library of written reports about forensic examinations. Special features of the web framework, in particular a report annotation tool, support the design of various individual and group learning activities that focus on the development of knowledge and competence in dealing with reporting and communication challenges in the students' future areas of professional activity.

Cambios en el uso y la concepción de las TIC, implementando el Mobile Learning.

This research analyses the actual use and conception of the ICT mobility that a life long learning group of students have. The students have participated in a Mobile Learning experience along an online postgraduate course, which was designed under a traditional e-learning perspective. The students received a tablet PC (iPad) in order to work at the course and also to use it in their personal and professional life. A complete and original pre-test / post-test questionnaire was applied before and after the course. This instrument was scientifically validated. Thru the questionnaire, uses tendency and students perceptions were studied. Frequencies, purposes, habits of use and valuation, as well as the device"s integration into their personal, social and professional life were studied. The analysis intents to apply the 'Social Technographics Profile" by Bernoff (2010) to classify, by profile groups, the users of the actual Internet. Finally a reflexion of the reasons and limits of the theory, in this study, and also the relation to reality is presented. The Inter-coding reliability and validity shows the possibility of applying the instrument on wider samples in order to get a closer look to the uses and actual conceptions of the ubiquitous ICTs.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Cost-utility analysis of a three-month exercise programme vs usual care following multidisciplinary rehabilitation for chronic low back pain.

OBJECTIVE: To assess the cost-utility of an exercise programme vs usual care after functional multidisciplinary rehabilitation in patients with chronic low back pain. DESIGN: Cost-utility analysis alongside a randomized controlled trial. SUBJECTS/PATIENTS: A total of 105 patients with chronic low back pain. METHODS: Chronic low back pain patients completing a 3-week functional multidisciplinary rehabilitation were randomized to either a 3-month exercise programme (n = 56) or usual care (n = 49). The exercise programme consisted of 24 training sessions during 12 weeks. At the end of functional multidisciplinary rehabilitation and at 1-year follow-up quality of life was measured with the SF-36 questionnaire, converted into utilities and transformed into quality--adjusted life years. Direct and indirect monthly costs were measured using cost diaries. The incremental cost-effectiveness ratio was calculated as the incremental cost of the exercise programme divided by the difference in quality-adjusted life years between both groups. RESULTS: Quality of life improved significantly at 1-year follow-up in both groups. Similarly, both groups significantly reduced total monthly costs over time. No significant difference was observed between groups. The incremental cost-effectiveness ratio was 79,270 euros. CONCLUSION: Adding an exercise programme after functional multidisciplinary rehabilitation compared with usual care does not offer significant long-term benefits in quality of life and direct and indirect costs.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Appropriateness of therapy for active Crohn's disease: results of a multidisciplinary international expert panel (EPACT II)

Introduction: The development of novel therapies and the increasing number of trials testing management strategies for luminal Crohn's disease (CD) have not filled all the gaps in our knowledge. Thus, in clinical practice, many decisions for CD patients need to be taken without high quality evidence. For this reason, a multidisciplinary European expert panel followed the RAND method to develop explicit criteria for the management of individual patients with active, steroid-dependent (ST-D) and steroid-refractory (ST-R) CD. Methods: Twelve international experts convened in Geneva, Switzerland in December 2007, to rate explicit clinical scenarios, corresponding to real daily practice, on a 9-point scale according to the literature evidence and their own expertise. Median ratings were stratified into three categories: appropriate (7-9), uncertain (4-6) and inappropriate (1-3). Results: Overall, panelists rated 296 indications pertaining to mild-to-moderate, severe, ST-D, and ST-R CD. In anti-TNF naïve patients, budesonide and prednisone were found appropriate for mildmoderate CD, and infliximab (IFX) when those had previously failed or had not been tolerated. In patients with prior success with IFX, this drug with or without co-administration of a thiopurine analog was favored. Other anti-TNFs were appropriate in case of intolerance or resistance to IFX. High doses steroids, IFX or adalimumab were appropriate in severe active CD. Among 105 indications for ST-D or ST-R disease, the panel considered appropriate the thiopurine analogs, methotrexate, IFX, adalimumab and surgery for limited resection, depending on the outcome of prior therapies. Anti-TNFs were generally considered appropriate in ST-R. Conclusion: Steroids, including budesonide for mild-to-moderate CD, remain first-line therapies in active luminal CD. Anti-TNFs, in particular IFX with respect to the amount of available evidence, remain second-line for most indications. Thiopurine analogs are preferred to anti-TNFs when steroids are not appropriate, except when anti-TNFs were previously successful. These recommendations are available online (www.epact.ch). A prospective evaluation of these criteria in a large database in Switzerland in underway to validate these criteria.

De la perception à l'apprentissage [From perception to learning]

Dans le domaine de la perception, l'apprentissage est contraint par la présence d'une architecture fonctionnelle constituée d'aires corticales distribuées et très spécialisées. Dans le domaine des troubles visuels d'origine cérébrale, l'apprentissage d'un patient hémi-anopsique ou agnosique sera limité par ses capacités perceptives résiduelles, mais un déficit de reconnaissance visuelle de nature apparemment perceptive, peut également être associé à une altération des représentations en mémoire à long terme. Des réseaux neuronaux distincts pour la reconnaissance - cortex temporal - et pour la localisation des sons - cortex pariétal - ont été décrits chez l'homme. L'étude de patients cérébro-lésés confirme le rôle des indices spatiaux dans un traitement auditif explicite du « where » et dans la discrimination implicite du « what ». Cette organisation, similaire à ce qui a été décrit dans la modalité visuelle, faciliterait les apprentissages perceptifs. Plus généralement, l'apprentissage implicite fonde une grande partie de nos connaissances sur le monde en nous rendant sensible, à notre insu, aux règles et régularités de notre environnement. Il serait impliqué dans le développement cognitif, la formation des réactions émotionnelles ou encore l'apprentissage par le jeune enfant de sa langue maternelle. Le caractère inconscient de cet apprentissage est confirmé par l'étude des temps de réaction sériels de patients amnésiques dans l'acquisition d'une grammaire artificielle. Son évaluation pourrait être déterminante dans la prise en charge ré-adaptative. [In the field of perception, learning is formed by a distributed functional architecture of very specialized cortical areas. For example, capacities of learning in patients with visual deficits - hemianopia or visual agnosia - from cerebral lesions are limited by perceptual abilities. Moreover a visual deficit in link with abnormal perception may be associated with an alteration of representations in long term (semantic) memory. Furthermore, perception and memory traces rely on parallel processing. This has been recently demonstrated for human audition. Activation studies in normal subjects and psychophysical investigations in patients with focal hemispheric lesions have shown that auditory information relevant to sound recognition and that relevant to sound localisation are processed in parallel, anatomically distinct cortical networks, often referred to as the "What" and "Where" processing streams. Parallel processing may appear counterintuitive from the point of view of a unified perception of the auditory world, but there are advantages, such as rapidity of processing within a single stream, its adaptability in perceptual learning or facility of multisensory interactions. More generally, implicit learning mechanisms are responsible for the non-conscious acquisition of a great part of our knowledge about the world, using our sensitivity to the rules and regularities structuring our environment. Implicit learning is involved in cognitive development, in the generation of emotional processing and in the acquisition of natural language. Preserved implicit learning abilities have been shown in amnesic patients with paradigms like serial reaction time and artificial grammar learning tasks, confirming that implicit learning mechanisms are not sustained by the cognitive processes and the brain structures that are damaged in amnesia. In a clinical perspective, the assessment of implicit learning abilities in amnesic patients could be critical for building adapted neuropsychological rehabilitation programs.]

La cohorte sepsis lausannoise: une opportunité de développer la recherche dans le Réseau latin de médecine intensive [Lausanne Cohort of septic patients as an opportunity to develop multidisciplinary research within the Swiss Latin Network of Intensive Care Medicine]

Despite recent medical progresses in patient support, the mortality of sepsis remains high. Recently, new supporting strategies were proposed to improve outcome. Whereas such strategies are currently considered as standard of care, their real impact on mortality, morbidity, length of stay, and hence, health care resources utilization has been only weakly evaluated so far. Obviously, there is a critical need for epidemiologic surveys of sepsis to better address these major issues. The Lausanne Cohort of septic patients aims at building a large clinical, biological and microbiological database that will be used as a multidisciplinary research platform to study the various pathogenic mechanisms of sepsis in collaboration with the various specialists. This could be an opportunity to strengthen the collaboration within the Swiss Latin network of Intensive Care Medicine.

Per aprendre anglès, quan abans millor? L'aprenentatge de l'anglès com a llengua estrangera. When Learning English, is it a case of 'The earlier the better?' Learning English as a foreign Language

En contra del que molts pares i mestres pensen, i en contra del que s'ha trobat en contextos d'adquisició natural de segones llengües i d'immersió, començar al més aviat possible l'aprenentatge d'idiomes, sobretot pel que fa a les tasques cognitivament més exigents, com l'escriptura, no sembla l'opció més eficaç. Els resultats del Grup de Recerca en Adquisició de Llengües (GRAL) estudià aprenents d'anglès que començaren als vuit i onze anys i trobaren, sistemàticament, que a llarg termini, després de les mateixes hores d'instrucció, eren els més grans, que havien començat als onze anys, els que obtenien millors resultats en totes les proves orals i escrites d'anglès excepte en alguna de prova de reconeixement fonètic. La maduresa cognitiva dels alumnes més grans i els diferents mecanismes d¿aprenentatge implícit i explícit entre els nens més petits i els adults ajuden a explicar per què només començar abans l'aprenentatge d'idiomes no sembla suficient per obtenir millors resultats.

Deep learning via semi-supervised embedding

We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multilayer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques.

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data

As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches - with the help of Bayesian networks - for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).

Embodying feminist research: learning from action research, political practices, diffractions and collective knowledge.

In the past three decades, feminists and critical theorists have discussed and argued the importance of deconstructing and problematizing social science research methodology in order to question normalized hierarchies concerning the production of knowledge and the status of truth claims. Nevertheless, often, these ideas have basically remained theoretical propositions not embodied in research practices. In fact there is very little published discussion about the difficulties and limits of their practical application. In this paper we introduce some interconnected reflections starting from two different but related experiences of embodying 'feminist activist research'. Our aim is to emphasise the importance of attending to process, making mistakes and learning during fieldwork, as well as experimenting with personalized forms of analysis, such as the construction of narratives and the story-telling process.

Embodying feminist research: learning from action research, political practices, diffractions and collective knowledge.

In the past three decades, feminists and critical theorists have discussed and argued the importance of deconstructing and problematizing social science research methodology in order to question normalized hierarchies concerning the production of knowledge and the status of truth claims. Nevertheless, often, these ideas have basically remained theoretical propositions not embodied in research practices. In fact there is very little published discussion about the difficulties and limits of their practical application. In this paper we introduce some interconnected reflections starting from two different but related experiences of embodying 'feminist activist research'. Our aim is to emphasise the importance of attending to process, making mistakes and learning during fieldwork, as well as experimenting with personalized forms of analysis, such as the construction of narratives and the story-telling process.