Independent AND-parallel implementation of narrowing

We present a parallel graph narrowing machine, which is used to implement a functional logic language on a shared memory multiprocessor. It is an extensión of an abstract machine for a purely functional language. The result is a programmed graph reduction machine which integrates the mechanisms of unification, backtracking, and independent and-parallelism. In the machine, the subexpressions of an expression can run in parallel. In the case of backtracking, the structure of an expression is used to avoid the reevaluation of subexpressions as far as possible. Deterministic computations are detected. Their results are maintained and need not be reevaluated after backtracking.

A technique for recursive invariance detection and selective program specialization

This paper presents a technique for achieving a class of optimizations related to the reduction of checks within cycles. The technique uses both Program Transformation and Abstract Interpretation. After a ñrst pass of an abstract interpreter which detects simple invariants, program transformation is used to build a hypothetical situation that simpliñes some predicates that should be executed within the cycle. This transformation implements the heuristic hypothesis that once conditional tests hold they may continué doing so recursively. Specialized versions of predicates are generated to detect and exploit those cases in which the invariance may hold. Abstract interpretation is then used again to verify the truth of such hypotheses and conñrm the proposed simpliñcation. This allows optimizations that go beyond those possible with only one pass of the abstract interpreter over the original program, as is normally the case. It also allows selective program specialization using a standard abstract interpreter not speciñcally designed for this purpose, thus simplifying the design of this already complex module of the compiler. In the paper, a class of programs amenable to such optimization is presented, along with some examples and an evaluation of the proposed techniques in some application áreas such as floundering detection and reducing run-time tests in automatic logic program parallelization. The analysis of the examples presented has been performed automatically by an implementation of the technique using existing abstract interpretation and program transformation tools.

An abstract machine based execution model for computer architecture design and efficient implementation of logic programs in parallel.

The term "Logic Programming" refers to a variety of computer languages and execution models which are based on the traditional concept of Symbolic Logic. The expressive power of these languages offers promise to be of great assistance in facing the programming challenges of present and future symbolic processing applications in Artificial Intelligence, Knowledge-based systems, and many other areas of computing. The sequential execution speed of logic programs has been greatly improved since the advent of the first interpreters. However, higher inference speeds are still required in order to meet the demands of applications such as those contemplated for next generation computer systems. The execution of logic programs in parallel is currently considered a promising strategy for attaining such inference speeds. Logic Programming in turn appears as a suitable programming paradigm for parallel architectures because of the many opportunities for parallel execution present in the implementation of logic programs. This dissertation presents an efficient parallel execution model for logic programs. The model is described from the source language level down to an "Abstract Machine" level suitable for direct implementation on existing parallel systems or for the design of special purpose parallel architectures. Few assumptions are made at the source language level and therefore the techniques developed and the general Abstract Machine design are applicable to a variety of logic (and also functional) languages. These techniques offer efficient solutions to several areas of parallel Logic Programming implementation previously considered problematic or a source of considerable overhead, such as the detection and handling of variable binding conflicts in AND-Parallelism, the specification of control and management of the execution tree, the treatment of distributed backtracking, and goal scheduling and memory management issues, etc. A parallel Abstract Machine design is offered, specifying data areas, operation, and a suitable instruction set. This design is based on extending to a parallel environment the techniques introduced by the Warren Abstract Machine, which have already made very fast and space efficient sequential systems a reality. Therefore, the model herein presented is capable of retaining sequential execution speed similar to that of high performance sequential systems, while extracting additional gains in speed by efficiently implementing parallel execution. These claims are supported by simulations of the Abstract Machine on sample programs.

ESP in Spain: goals, achievements and prospects

This paper aims to describe the development of English for Specific Purposes (ESP) as specialised language study and research at tertiary level in Spain over the past twenty years. The year 1992 is chosen as a starting point because AELFE, the Association of Languages for Specific Purposes, was founded in Madrid at the time. As more members from other countries have joined in, this Association has served as an academic landmark for the development of ESP within the umbrella of applied linguistics. ESP has reflected the social changes, educational shifts, linguistic trends, and technological innovations involved in academic and professional contexts. The evolution of the specialised language practitioner’s scenarios and communicative situations has turned ESP into a lively and stimulating action, though not lacking in controversy, e.g., a general increase in the ESP teacher’s workload. Different lines of work and research have been followed from the inception of AELFE until the implementation of the Bologna agreement in our universities. The examination of such variables is conducted in the light of some quantitative and qualitative findings.

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

Proposal for a semantic hierarchy of terminological metaphors in science and technology

Cognitive linguistics is considered as one of the most appropriate approaches to the study of scientific and technical language formation and development, where metaphor is accepted to play an essential role. This paper, based on the Cognitive Theory of Metaphor, takes as the starting point the terminological metaphors established in the research project METACITEC(Note 1), which was developed with the purpose of unfolding constitutive metaphors and their function in the language of science and technology. After the analysis of metaphorical terms and using a mixed corpus from the fields of Agriculture, Geology, Mining, Metallurgy, and other related technical fields, this study presents a proposal for a hierarchy of the selected metaphors underlying the scientific conceptual system, based on the semantic distance found in the projection from the source domain to the target domain. We argue that this semantic distance can be considered as an important parameter to take into account in order to establish the metaphoricity of science and technology metaphorical terms. The findings contribute to expand on the CTM stance that metaphor is a matter of cognition by reviewing the abstract-concrete conceptual relationship between the target and source domains, and to determine the role of human creativity and imagination in the language of science and technology configuration

Evaluating a web-based clinical decision support system for language disorders screening in a nursery school

Background: Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder. Objective: This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care. Methods: This paper details the evaluation results of the ?Gades? CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child?s language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results. Results: The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic abilities. The refinement of the KB will address these issues and include the requested improvements, with the support of the experts who took part in the original KB development. Conclusions: This research demonstrated the benefit of a Web-based CDSS to monitor children?s neurodevelopment via the early detection of language delays at a nursery school. Current next steps focus on the design of a model that includes pseudo auto-learning capacity, supervised by experts.

Enabling Language Resources to expose translations as linked data on the web

Language resources, such as multilingual lexica and multilingual electronic dictionaries, contain collections of lexical entries in several languages. Having access to the corresponding explicit or implicit translation relations between such entries might be of great interest for many NLP-based applications. By using Semantic Web-based techniques, translations can be available on the Web to be consumed by other (semantic enabled) resources in a direct manner, not relying on application-specific formats. To that end, in this paper we propose a model for representing translations as linked data, as an extension of the lemon model. Our translation module represents some core information associated to term translations and does not commit to specific views or translation theories. As a proof of concept, we have extracted the translations of the terms contained in Terminesp, a multilingual terminological database, and represented them as linked data. We have made them accessible on the Web both for humans (via a Web interface) and software agents (with a SPARQL endpoint).

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Technology independent honeynet description language

Several languages have been proposed for the task of describing networks of systems, either to help on managing, simulate or deploy testbeds for testing purposes. However, there is no one specifically designed to describe the honeynets, covering the specific characteristics in terms of applications and tools included in the honeypot systems that make the honeynet. In this paper, the requirements of honeynet description are studied and a survey of existing description languages is presented, concluding that a CIM (Common Information Model) match the basic requirements. Thus, a CIM like technology independent honeynet description language (TIHDL) is proposed. The language is defined being independent of the platform where the honeynet will be deployed later, and it can be translated, either using model-driven techniques or other translation mechanisms, into the description languages of honeynet deployment platforms and tools. This approach gives flexibility to allow the use of a combination of heterogeneous deployment platforms. Besides, a flexible virtual honeynet generation tool (HoneyGen) based on the approach and description language proposed and capable of deploying honeynets over VNX (Virtual Networks over LinuX) and Honeyd platforms is presented for validation purposes.

Genes, peoples, and languages

The genetic history of a group of populations is usually analyzed by reconstructing a tree of their origins. Reliability of the reconstruction depends on the validity of the hypothesis that genetic differentiation of the populations is mostly due to population fissions followed by independent evolution. If necessary, adjustment for major population admixtures can be made. Dating the fissions requires comparisons with paleoanthropological and paleontological dates, which are few and uncertain. A method of absolute genetic dating recently introduced uses mutation rates as molecular clocks; it was applied to human evolution using microsatellites, which have a sufficiently high mutation rate. Results are comparable with those of other methods and agree with a recent expansion of modern humans from Africa. An alternative method of analysis, useful when there is adequate geographic coverage of regions, is the geographic study of frequencies of alleles or haplotypes. As in the case of trees, it is necessary to summarize data from many loci for conclusions to be acceptable. Results must be independent from the loci used. Multivariate analyses like principal components or multidimensional scaling reveal a number of hidden patterns and evaluate their relative importance. Most patterns found in the analysis of human living populations are likely to be consequences of demographic expansions, determined by technological developments affecting food availability, transportation, or military power. During such expansions, both genes and languages are spread to potentially vast areas. In principle, this tends to create a correlation between the respective evolutionary trees. The correlation is usually positive and often remarkably high. It can be decreased or hidden by phenomena of language replacement and also of gene replacement, usually partial, due to gene flow.

Habilidades de processamento fonológico e de escrita em crianças com distúrbio específico de linguagem: um estudo comparativo com a normalidade

Introdução: Crianças com distúrbio específico de linguagem (DEL) são propensas a apresentar dificuldade no processo de alfabetização devido às múltiplas alterações de linguagem que possuem. Este estudo comparou e caracterizou o desempenho de crianças com DEL e em desenvolvimento típico de linguagem em atividades de aliteração, rima, memória de curto prazo fonológica, ditado de palavras e de pseudopalavras. A principal hipótese do estudo era de que o grupo DEL apresentaria desempenho inferior do que o grupo em desenvolvimento típico em todas as habilidades estudadas. Método: Participaram do estudo 12 crianças com DEL (GP) e 48 em desenvolvimento típico (GC) com idade entre 7 anos e 9 anos e 11 meses. Todos os sujeitos cursavam o 2º ou 3º ano do ensino fundamental I e apresentavam audição e rendimento intelectual não-verbal preservados. Para a seleção dos grupos foram utilizadas medidas de vocabulário receptivo, fonologia e nível socioeconômico. Já as medidas experimentais avaliadas foram testes padronizados de aliteração, rima, memória de curto prazo fonológica e a aplicação de um ditado de palavras e de pseudopalavras elaborados para esta pesquisa. Resultados: ambos os grupos apresentaram pior desempenho em tarefas de rima do que de aliteração e o GP apresentou desempenho inferior em ambas as tarefas quando comparado ao GC. A análise dos distratores nas atividades de aliteração e rima apontou que em tarefas de aliteração, o GP cometeu mais erros de tipologia semântico enquanto na prova de rima foram mais erros de tipologia fonológico. O GP obteve desempenho inferior ao GC nas avaliações da memória de curto prazo fonológica, ditado de palavras e de pseudopalavras. O GP evidenciou maior dificuldade no ditado de pseudopalavras no que no de palavras e o GC não apresentou diferença significativa no desempenho dos ditados. No ditado de palavras, o GP cometeu mais erros na palavra toda enquanto no ditado de pseudopalavras ocorreram mais erros na palavra toda e na sílaba final. Na comparação do desempenho dos grupos de acordo com a escolaridade, notou-se que os sujeitos do GC do 2º e 3º ano não evidenciaram diferença significativa em seu desempenho nas tarefas, enquanto os sujeitos do GP do 3º ano apresentaram melhor desempenho do que os do 2º ano em todas as medidas experimentais, com exceção da memória de curto prazo fonológica. Conclusões: o GP apresentou dificuldade em tarefas de processamento fonológico e de escrita que foram realizadas com relativa facilidade pelo GC. Os sujeitos com DEL evidenciaram uma análise mais global dos estímulos apresentados nas tarefas de consciência fonológica, o que os fez desprezar aspectos segmentais importantes. A dificuldade em abordar as informações de modo analítico, somado a alterações linguísticas e do processamento fonológico, levou o GP a apresentar maior taxa de erros nas tarefas de ditado. Apesar das alterações apontadas, os sujeitos do GP do 3º ano obtiveram melhor desempenho do que os do 2º ano em todas as habilidades com exceção da memória de curto prazo fonológica, que é sua marca clínica. Estes dados reforçam a necessidade do diagnóstico e intervenção precoces para esta população, onde as habilidades abordadas neste estudo devem ser incluídas no processo terapêutico

Molecular Genetic Basis of Opposite Gene by Environment Interactions in Reading Disability and Attention Deficit/Hyperactivity Disorder

The goal of this study is to better understand the genetic basis of Reading Disability (RD) and Attention Deficit Hyperactivity Disorder (ADHD) by examining molecular G x E interactions with parental education for each disorder. Research indicates that despite sharing genetic risk factors, RD and ADHD are influenced by different types of G x E interactions with parental education - a diathesis stress interaction in the case of ADHD and a bioecological interaction in RD. In order to resolve this apparent paradox, we conducted a preliminary study using behavioral genetic methods to test for G x E interactions in RD and the inattentive subtype of ADHD (ADHD-I) in the same sample of monozygotic and dizygotic Colorado Learning Disabilities Research Center same-sex twin pairs (DeFries et al., 1997), and our findings were consistent with the literature. We posited a genetic hypothesis for this opposite pattern of interactions, which suggests that only genes specific to each disorder enter into these opposite interactions, not the shared genes underlying their comorbidity. This study sought to further investigate this paradox using molecular genetics methods. We examined multiple candidate genes identified for RD or related language phenotypes and those identified for ADHD for G x E interactions with parental education. The specific aims of this study were as follows: 1) partition known risk alleles for RD and/or related language phenotypes and ADHD-I into those which are pleiotropic and non-pleiotropic by testing each risk allele for association with both RD and ADHD-I, 2) explore the main effects of parental education on both RD and ADHD-I, 3) address G-E correlations, and 4) conduct exploratory G x E interaction analyses in order to test the genetic hypothesis. Analyses suggested a number of pleiotropic genes that influence both RD and ADHD; however, results did not remain after correcting for multiple comparisons. Although exploratory G x E interaction findings were not significant after multiple comparison correction, results suggested a G x E interaction in the bioecological direction with KIAA0319, parental education, and ADHD-I. Given the limited power in the current study, replication of these findings with larger samples is necessary.

Capacity-building through English language training to improve long-term resettlement success: A case study of the refugees along the Thailand-Burma border

Achieving long-term resettlement success is a challenge for many refugees seeking to restart their lives after displacement and being uprooted from their lives. Refugees must deal with finding employment, integrating into a society immensely different from what they have known their whole lives, and starting over from scratch. Learning a new language enables refugees to progress towards integration and long-term resettlement success, however, resettled refugees face a multitude of barriers in the U.S. to accessing language classes and attaining English proficiency. This study seeks to bridge this problem by exploring the possibilities of implementing a standardized language training program in the refugee camps to better prepare refugees for resettlement. A case study of the refugees along the Thai-Burma border demonstrated the significance of learning English in the camps on eventual English proficiency as well as the need for increased partnerships to overcome the barriers of lack of motivation and lack of funding. The author explores the possibilities of implementing a language training program in the camps by determining need, interest, barriers, and perceptions through the use of interviews, surveys, and focus groups of camp refugees, resettled refugees, and key organizational representatives. The significance of these results offers the possibility of leveraging and unlocking resettlement as a durable solution for more of the world's refugees in protracted situations.

Chinese Intellectuals and China`s Policy toward Japan

This dissertation aims at integrating two scholarships: state-society relation studies and Chinese foreign policy analysis. I created Two-level Perception Gap Model to analyze different intellectual groups' relations with party-state by confirming Chinese intellectuals play a role in CFP making in general, China's Japan policy in particular. This model is an alternative approach, instead of conventional wisdom patron-client approach, to explain and analyze the pluralized intellectual-state relations in China. This model first analyzed the role of two intellectual groups, namely think tank scholars and popular nationalist, in China's Japan policy making, and then based on these analyses it explains the interactional patterns between these two intellectual groups and party-state. I used three case studies, which represented different types of issue, Chinese attitude toward the U.S.-Japan alliance and the Japanese defense policy, the controversy over the Yasukuni Shrine Visit, and the territorial dispute over the Diaoyu/Senkaku Islands, to examine this model. First, I examined think tank scholar groups and the extent they influenced "core interest issue and sensitive issue (Issue 1)," Chinese attitude toward the U.S.-Japan alliance and the Japanese defense policy, and their international patterns with party-state. Chapter 3 compares the responses of Chinese officials to the changes in the defense policy of Japan to the analyses from the think tank scholars. As the model assumes, results show that think tank scholars' analyses are consistent with China's policy position; nevertheless, it is difficult to confirm their analyses have influence on Chinese attitude toward the U.S.-Japan alliance and the Japanese defense policy. Based on the analysis of journal articles, most articles do not provide policy suggestions or simply provide suggestions that do not deviate from the policy. As Gu's theory of pluralist institutionalism and my hypothesis points out, most think tank scholars are establishment intellectuals so they tend to be self-disciplined. Second, this model provide a new concept "patriotic dilemma" for analyzing the challenge and constraints brought by popular nationalist discourses and public mobilization to Chinese foreign policy decision makers. Chapter 4 investigated the cases study of the controversy over the Yasukuni Shrine Visit, defined as "major/minor interest issue/ sensitive issue (Issue 3)," and the discourses from the popular nationalist, mainly focusing on anti-Japanese activists. The chapter also observes their influence on nationalist public opinions and analyzes how the nationalist public opinions constrain the policy choices among decision makers. Results strongly supported the hypothesis of patriotic dilemma that, although the popular nationalist group and public opinions constrained the policy choices of Chinese decision makers in the short term, they were unable to change the fundamental policy direction. Third, chapter 5 also focuses on anti-Japanese activists and examines the model with the case of the territorial dispute over the Diaoyu/Senkaku Islands. The result supported that hypothesis that China's policy change was not because of the influence from popular nationalist's discourses or public opinions but because of the change of priority of this issue, from major/minor interest issue to core interest issue. These two chapters also indicate that the patron-client model is unable to describe the popular nationalist. An alternative approach, such as the concept "patriotic dilemma" is needed to describe the relations between the popular nationalist and the government.