The effect of bilingual representation in prose reading on explicit and implicit memory

Chinese-English bilingual students were randomly assigned to three reading conditions: In the English-English (E-E) condition (n = 44), a text in English was read twice; in the English-Chinese (E-C) condition (n = 30), the English text was read first and its Chinese translation was read second; in the Chinese-English (C-E) condition (n = 30), the Chinese text was read first and English second. An expected explicit memory test on propositions in the format of sentence verification was given followed by an unexpected implicit memory test on unfamiliar word-forms.^ Analyses of covariance were conducted with explicit and implicit memory scores as the dependent variables, reading condition (bilingual versus monolingual) as the independent variable, and TOEFL reading score as the covariate.^ The results showed that the bilingual reading groups outperformed the monolingual reading group on explicit memory tested by sentence-verification but not on implicit memory tested by forced-choice word-identification, implying that bilingual representation facilitates explicit memory of propositional information but not implicit memory of lexical forms. The findings were interpreted as consistent with separate bilingual memory-storage models and the implications of such models in the study of cognitive structures were discussed in relationship to issues of dual coding theory, multiple memory systems, and the linguistic relativity philosophy. ^

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Performance analysis and tuning in multicore environments

Performance analysis is the task of monitor the behavior of a program execution. The main goal is to find out the possible adjustments that might be done in order improve the performance. To be able to get that improvement it is necessary to find the different causes of overhead. Nowadays we are already in the multicore era, but there is a gap between the level of development of the two main divisions of multicore technology (hardware and software). When we talk about multicore we are also speaking of shared memory systems, on this master thesis we talk about the issues involved on the performance analysis and tuning of applications running specifically in a shared Memory system. We move one step ahead to take the performance analysis to another level by analyzing the applications structure and patterns. We also present some tools specifically addressed to the performance analysis of OpenMP multithread application. At the end we present the results of some experiments performed with a set of OpenMP scientific application.

Embedded Monitoring Server

This study investigates, designs, and implements an inexpensive application that allows local and remote monitoring of a home. The application consists of an array of sensors for monitoring different conditions in a home environment and also for accessing the devices that might be connected to the system. Only a few sensors are initially involved in this study and information about the temperature level, forced entry detection, smoke and water leakage detection can be obtained at any time from any location with an Internet connection. The application software (coded in C language) runs on an embedded system which is basically a wireless Linksys router running on a GNU/Linux based firmware for embedded systems. Interaction between the sensors and the application software is achieved through an implemented sensor interfacing circuit. The communication with the sensor interfacing unit is done through the serial port, and accessibility of the connected sensors is achieved through a telnet client. The sensors can be accessed from local and remote locations with the sensors giving reliable information. The resulting application shows that it is possible to use the router for other applications other than what it is intended for.

Exploration du lien entre la qualité de la mentalisation et l'efficacité du rappel autobiographique

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Alteración de la atención y concentración en estudiantes de medicina que realizan turnos de noche

El sueño, es indispensable para la recuperación, física, mental y de procesos como la consolidación de memoria, atención y lenguaje. La privación de sueño (PS) incide en la atención y concentración. La PS es inherente a la formación médica, pero no es claro el papel de los turnos nocturnos en estudiantes, porque no cumplen con un objetivo académico, pero hay relación con disminución de la salud, productividad, accidentes, y alteraciones en diversas actividades. Está descrito el impacto de la PS sobre la capacidad de aprendizaje y aspectos como el ánimo y las relaciones interpersonales. MÉTODOS: Se realizó un estudio analítico observacional de cohorte longitudinal, con tres etapas de medición a 180 estudiantes de Medicina de la Universidad del Rosario, que evaluó atención selectiva y concentración mediante la aplicación de la prueba d2, validada internacionalmente para tal fin. RESULTADOS: Se estudiaron 180 estudiantes, 115 mujeres, 65 hombres, entre los 18 y 26 años (promedio 21). Al inicio del estudio dormían en promedio 7,9 horas, cifra que se redujo a 5,8 y 6,3 en la segunda y tercera etapa respectivamente. El promedio de horas de sueño nocturno, disminuyó en el segundo y tercer momento (p<0,001); Además se encontró mediante la aplicación de la prueba d2, que hubo correlación significativa directa débil, entre el promedio de horas de sueño, y el promedio del desempeño en la prueba (r=0.168, p=0.029) CONCLUSIONES: La PS, con períodos de sueño menores a 7,2 horas, impactan de manera importante la atención selectiva, la concentración

Efectividad del tratamiento con neurofeedback en trastornos del estado del ánimo, ansiedad y fibromialgia: una revisión de literatura

El neurofeedback es una técnica no invasiva en la que se pretende corregir, mediante condicionamiento operante, ondas cerebrales que se encuentren alteradas en el electroencefalograma. Desde 1967, se han conducido numerosas investigaciones relacionadas con los efectos de la técnica en el tratamiento de alteraciones psicológicas. Sin embargo, a la fecha no existen revisiones sistemáticas que reúnan los temas que serán aquí tratados. El aporte de este trabajo es la revisión de 56 artículos, publicados entre los años 1995 y 2013 y la evaluación metodológica de 29 estudios incluidos en la revisión. La búsqueda fue acotada a la efectividad del neurofeedback en el tratamiento de depresión, ansiedad, trastorno obsesivo compulsivo (TOC), ira y fibromialgia. Los hallazgos demuestran que el neurofeedback ha tenido resultados positivos en el tratamiento de estos trastornos, sin embargo, es una técnica que aún está en desarrollo, con unas bases teóricas no muy bien establecidas y cuyos resultados necesitan de diseños metodológicamente más sólidos que ratifiquen su validez.

Examen neurológico

Se realizó un capítulo sobre la descripción del examen neurológico como herramienta principal en el abordaje del paciente con patología neurológica.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Norms-based simulation for personalised service provision

User interfaces have the primary role of enabling access to information meeting individual users' needs. However, the user-systems interaction is still rigid, especially in support of complex environments where various types of users are involved. Among the approaches for improving user interface agility, we present a normative approach to the design interfaces of web applications, which allow delivering users personalized services according to parameters extracted from the simulation of norms in the social context. A case study in an e-Government context is used to illustrate the implications of the approach.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

L3 acquisition: a focus on cognitive approaches

Interest in third language (L3) acquisition has increased exponentially in recent years, due to its potential to inform long-lasting debates in theoretical linguistics, language acquisition and psycholinguistics. Researchers investigating child and adult L3 acquisition have, from the very beginning, considered the many different cognitive factors that constrain and condition the initial state and development of newly acquired languages, and their models have duly evolved to incorporate insights from the most recent findings in psycholinguistics, neurolinguistics and cognitive psychology. The articles in this Special Issue of Bilingualism: Language and Cognition, in dealing with issues such as age of acquisition, attrition, relearning, cognitive economy or the reliance on different memory systems –to name a few–, provide an accurate portrayal of current inquiry in the field, and are a particularly fine example of how instrumental research in language acquisition and other cognitive domains can be to one another.