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.

Cuatro casos, cuatro historias de uso educativo de las TIC

[spa] Este informe presenta parte de los procesos y los resultados del proyecto de I+D+I: Políticas y prácticas en torno a las TIC en la enseñanza obligatoria: Implicaciones para la innovación y la mejora, parcialmente financiado por Ministerio de Ciencia e Innovación. SEJ2007-67562. Recoge los cuatro estudios de caso llevados a cabo en dos Institutos de Enseñanza Secundaria Obligatoria y dos centros de educación primaria de Cataluña, que nos han posibilitado elucidar el impacto de las políticas de uso de las TIC y de otras iniciativas políticas para favorecer la innovación y la mejora en cada centro, prestando especial atención a: (a) los temas organizativos relacionados con el tiempo y el espacio; (b) el desarrollo del currículum en el centro (visiones sobre el conocimiento, el aprendizaje, el papel del alumnado y el profesorado, el lugar de las TIC, etc.); (c) las condiciones de trabajo del profesorado (acceso a formación, espacios y prácticas de colaboración; desarrollo profesional); (d) los resultados del aprendizaje (valor intelectual, social y personal de lo aprendido; capacidad de transferencia para seguir aprendiendo

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.

Anesthesia prevents auditory perceptual learning.

BACKGROUND: An auditory perceptual learning paradigm was used to investigate whether implicit memories are formed during general anesthesia. METHODS: Eighty-seven patients who had an American Society of Anesthesiologists physical status of I-III and were scheduled to undergo an elective surgery with general anesthesia were randomly assigned to one of two groups. One group received auditory stimulation during surgery, whereas the other did not. The auditory stimulation consisted of pure tones presented via headphones. The Bispectral Index level was maintained between 40 and 50 during surgery. To assess learning, patients performed an auditory frequency discrimination task after surgery, and comparisons were made between the groups. General anesthesia was induced with thiopental and maintained with a mixture of fentanyl and sevoflurane. RESULTS: There was no difference in the amount of learning between the two groups (mean +/- SD improvement: stimulated patients 9.2 +/- 11.3 Hz, controls 9.4 +/- 14.1 Hz). There was also no difference in initial thresholds (mean +/- SD initial thresholds: stimulated patients 31.1 +/- 33.4 Hz, controls 28.4 +/- 34.2 Hz). These results suggest that perceptual learning was not induced during anesthesia. No correlation between the bispectral index and the initial level of performance was found (Pearson r = -0.09, P = 0.59). CONCLUSION: Perceptual learning was not induced by repetitive auditory stimulation during anesthesia. This result may indicate that perceptual learning requires top-down processing, which is suppressed by the anesthetic.