Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Electrical neuroimaging of memory discrimination based on single-trial multisensory learning.

Multisensory experiences influence subsequent memory performance and brain responses. Studies have thus far concentrated on semantically congruent pairings, leaving unresolved the influence of stimulus pairing and memory sub-types. Here, we paired images with unique, meaningless sounds during a continuous recognition task to determine if purely episodic, single-trial multisensory experiences can incidentally impact subsequent visual object discrimination. Psychophysics and electrical neuroimaging analyses of visual evoked potentials (VEPs) compared responses to repeated images either paired or not with a meaningless sound during initial encounters. Recognition accuracy was significantly impaired for images initially presented as multisensory pairs and could not be explained in terms of differential attention or transfer of effects from encoding to retrieval. VEP modulations occurred at 100-130ms and 270-310ms and stemmed from topographic differences indicative of network configuration changes within the brain. Distributed source estimations localized the earlier effect to regions of the right posterior temporal gyrus (STG) and the later effect to regions of the middle temporal gyrus (MTG). Responses in these regions were stronger for images previously encountered as multisensory pairs. Only the later effect correlated with performance such that greater MTG activity in response to repeated visual stimuli was linked with greater performance decrements. The present findings suggest that brain networks involved in this discrimination may critically depend on whether multisensory events facilitate or impair later visual memory performance. More generally, the data support models whereby effects of multisensory interactions persist to incidentally affect subsequent behavior as well as visual processing during its initial stages.

Water chemistry and isotope composition of the Acquarossa thermal system, Ticino, Switzerland

The thermal springs of Acquarossa and the nearby mineral springs of Soia have outlet temperatures of 12 degrees to 25 degrees C, TDS of 2290 to 3000 mg/kg and Ca-SO4 to Ca-SO4-HCO3 composition. Chemical geothermometers suggest reservoir temperatures close to 60 degrees C. P-CO2 values at depth are estimated to range from 0.3 to 2 bar. delta D and delta(18)O values indicate a meteoric origin and recharge elevations of 1600 +/- 150 m above sea level (a.s.l.) for these thermal and mineral waters. All these waters discharge from the overturned limb of the Simano nappe, probably dose to the contact between basement and underlying cover rocks. They therefore represent rain waters that descend slowly, heat at depth and locally rise relatively quickly to the surface, preserving part of their physical and chemical characteristics. (C) 1999 CNR. Published by Elsevier Science Ltd. All rights reserved.

Modes of acquisition of health literacy skills in informal learning contexts

In this article we try to analyze the learning processes of health literacy skills in informal contexts. We intend to broaden the understanding of the learning process beyond the formal contexts, thus contributing to the elucidation of health professionals on how individuals acquire and manage their knowledge in health matters. Given our goal, we use an analytic corpus constituted by one hundred autobiographical narratives written between 2006 and 2011, in educational contexts but with recognized potential for use in different scientific fields, including health. The results reveal the existence of three different types of modes of learning health literacy skills in informal context: : i) learning that takes place in action, in achieving daily tasks; ii) learning processes that result from problem solving; iii) learning that occurs in an unplanned manner, resulting from accidental circumstances and, in some cases, devoid of intentionality.

The art and learning patterns of knowing in nursing

Objective To identify the perception of the students about the use of art as a pedagogical strategy in learning the patterns of knowing in nursing; to identify the dimensions of each pattern valued in the analysis of pieces of art. Method Descriptive mixed study. Data collection used a questionnaire applied to 31 nursing students. Results In the analysis of the students’ discourse, it was explicit that empirical knowledge includes scientific knowledge, tradition and nature of care. The aesthetic knowledge implies expressiveness, subjectivity and sensitivity. Self-knowledge, experience, reflective attitude and relationships with others are the subcategories of personal knowledge and the moral and ethics support ethical knowledge. Conclusion It is possible to learn patterns of knowledge through art, especially the aesthetic, ethical and personal. It is necessary to investigate further pedagogical strategies that contribute to the learning patterns of nursing knowledge.

Effects of web-based electrocardiography simulation on strategies and learning styles

OBJECTIVETo identify the association between the use of web simulation electrocardiography and the learning approaches, strategies and styles of nursing degree students.METHODA descriptive and correlational design with a one-group pretest-posttest measurement was used. The study sample included 246 students in a Basic and Advanced Cardiac Life Support nursing class of nursing degree.RESULTSNo significant differences between genders were found in any dimension of learning styles and approaches to learning. After the introduction of web simulation electrocardiography, significant differences were found in some item scores of learning styles: theorist (p < 0.040), pragmatic (p < 0.010) and approaches to learning.CONCLUSIONThe use of a web electrocardiogram (ECG) simulation is associated with the development of active and reflexive learning styles, improving motivation and a deep approach in nursing students.

Assessment of the teaching-learning process in students of the health area: cardiopulmonary resuscitation maneuvers

OBJECTIVETo evaluate the skills and knowledge of undergraduate students in the health area on cardiopulmonary resuscitation maneuvers with the use of an automatic external defibrillator.METHODThe evaluation was performed in three different stages of the teaching-learning process. A theoretical and practical course was taught and the theoretical classes included demonstration. The evaluation was performed in three different stages of the teaching-learning process. Two instruments were applied to evaluate the skills (30-items checklist) and knowledge (40-questions written test). The sample comprised 84 students.RESULTSAfter the theoretical and practical course, an increase was observed in the number of correct answers in the 30-items checklist and 40-questions written test.CONCLUSIONAfter the theoretical class (including demonstration), only one of the 30-items checklist for skills achieved an index ≥ 90% of correct answers. On the other hand, an index of correct answers greater than 90% was achieved in 26 (86.7%) of the 30 items after a practical training simulation, evidencing the importance of this training in the defibrillation procedure.

Adaptação transcultural e validação da Readiness for Interprofessional Learning Scale no Brasil

RESUMO Objetivo Realizar a adaptação transcultural e a validação da versão de 29-itens daReadiness for Interprofessional Learning Scale (RIPLS) para língua portuguesa falada no Brasil. Método Foram adotadas cinco etapas: três traduções, síntese, três retrotraduções, avaliação por especialistas e pré-teste. A validação contou com 327 estudantes de 13 cursos de graduação de uma universidade pública. Foram realizadas análises paralelas com o software R e a análise fatorial utilizando Modelagem de Equações Estruturais. Resultados A análise fatorial resultou em uma escala de 27 itens e três fatores: Fator 1 – Trabalho em equipe e colaboração com 14 itens (1-9, 12-16), Fator 2 – Identidade profissional, oito itens (10, 11, 17, 19, 21-24), e Fator 3 – Atenção à saúde centrada no paciente, cinco itens (25-29). Alfa de Cronbach dos três fatores foi respectivamente: 0,90; 0,66; 0,75. Análise de variância mostrou diferenças significativas nas médias dos fatores dos grupos profissionais. Conclusão Foram identificadas evidências de validação da versão em português da RIPLS em sua aplicação no contexto nacional.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Strong minimax lower bounds for learning

Minimax lower bounds for concept learning state, for example, thatfor each sample size $n$ and learning rule $g_n$, there exists a distributionof the observation $X$ and a concept $C$ to be learnt such that the expectederror of $g_n$ is at least a constant times $V/n$, where $V$ is the VC dimensionof the concept class. However, these bounds do not tell anything about therate of decrease of the error for a {\sl fixed} distribution--concept pair.\\In this paper we investigate minimax lower bounds in such a--stronger--sense.We show that for several natural $k$--parameter concept classes, includingthe class of linear halfspaces, the class of balls, the class of polyhedrawith a certain number of faces, and a class of neural networks, for any{\sl sequence} of learning rules $\{g_n\}$, there exists a fixed distributionof $X$ and a fixed concept $C$ such that the expected error is larger thana constant times $k/n$ for {\sl infinitely many n}. We also obtain suchstrong minimax lower bounds for the tail distribution of the probabilityof error, which extend the corresponding minimax lower bounds.

Two-sided learning in New Keynesian models: Dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank(two-sided learning) in a New Keynesian framework in which both sides of the economyhave asymmetric and imperfect knowledge about the true data generating process. Weassume that all agents employ the data that they observe (which may be distinct fordifferent sets of agents) to form beliefs about unknown aspects of the true model ofthe economy, use their beliefs to decide on actions, and revise these beliefs througha statistical learning algorithm as new information becomes available. We study theshort-run dynamics of our model and derive its policy recommendations, particularlywith respect to central bank communications. We demonstrate that two-sided learningcan generate substantial increases in volatility and persistence, and alter the behaviorof the variables in the model in a significant way. Our simulations do not convergeto a symmetric rational expectations equilibrium and we highlight one source thatinvalidates the convergence results of Marcet and Sargent (1989). Finally, we identifya novel aspect of central bank communication in models of learning: communicationcan be harmful if the central bank's model is substantially mis-specified.