Strategies and interlanguage pragmatics: Explicit and comprehensive

Explicit instruction in strategies for interlanguage pragmatic learning is fundamental to the development of a comprehensive set of pragmatic abilities in the target language. In this article, we begin by providing an overview of previous work in the area of language learner strategies directed at the teaching and learning of pragmatics. We then offer an extension of Cohen’s (2005, 2014) framework of strategies for learning, using, and evaluating the use of interlanguage pragmatics in four domains: knowledge, analysis, subjectivity, and awareness (Sykes, Malone, Forrest, & Sadgic, forthcoming). Examples from current projects are provided to exemplify the critical importance of a strategies-based approach to the teaching and learning of interlanguage pragmatics. The article concludes with ideas for future research and implementation

Sensor Resource Management: Intelligent Multi-objective Modularized Optimization Methodology and Models

The importance of the optimal Sensor Resource Management (SRM) problem is growing. The number of Radar, EO/IR, Overhead Persistent InfraRed (OPIR), and other sensors with best capabilities, is limited in the stressing tasking environment relative to sensing needs. Sensor assets differ significantly in number, location, and capability over time. To determine on which object a sensor should collect measurements during the next observation period k, the known algorithms favor the object with the expected measurements that would result in the largest gain in relative information. We propose a new tasking paradigm OPTIMA for sensors that goes beyond information gain. It includes Sensor Resource Analyzer, and the Sensor Tasking Algorithm (Tasker). The Tasker maintains timing constraints, resolution, and geometric differences between sensors, relative to the tasking requirements on track quality and the measurements of object characterization quality. The Tasker does this using the computational intelligence approach of multi-objective optimization, which involves evolutionary methods

Researching Identity and L2 Pragmatics in Digital Stories: A Relational Account.

This study explores college EFL learners' construction of identity through the analysis of their pragmatic choices in digital stories, in which they narrated their relationship with another person they had helped in the past. More specifically, such choices were examined following Relational Dialectics Theory in learners' enactments of 'connection' with and 'autonomy' from this person. A specific view of identity in language education, the notion of 'relational work' in (im)politeness research, and a social semiotic framework were also employed in data analysis. Learners' pragmatic choices ranged from the selection of the topic of their narratives according to types of social bonds, to the use of specific semiotic resources to build identities in conflict episodes of their stories (i.e., positive identities for themselves and positive and negative identities for their relational partners). The construction of these identities paralleled relational parties' convergent and divergent moves towards connection and autonomy, revealing their relational work. Learners used different semiotic resources in resolution episodes, which enabled them to craft positive identities for themselves as experts, teachers, and learners as well as position their relational partner as a competent agent and shape the connection-autonomy dialectic as 'superiority-equality'

The California Cooperative Remote Sensing Project

The USDA, the California Department of Water Resources (CDWR), the Remote Sensing Research Program of the University of California (UCB) and NASA have completed a 4-yr cooperative project on the use of remote sensing in monitoring California agriculture. This report is a summary of the project and the final report of NASA's contribution to it. The cooperators developed procedures that combined the use of LANDSAT Multispectral Scanner imagery and digital data with good ground survey data for area estimation and mapping of the major crops in California. An inventory of the Central Valley was conducted as an operational test of the procedures. The satellite and survey data were acquired by USDA and UCB and processed by CDWR and NASA. The inventory was completed on schedule, thus demonstrating the plausibility of the approach, although further development of the data processing system is necessary before it can be used efficiently in an operational environment

Principled Approaches to Automatic Text Summarization

Automatic text summarization is a particularly challenging Natural Language Processing (NLP) task involving natural language understanding, content selection and natural language generation. In this thesis, we concentrate on the content selection aspect, the inherent problem of summarization which is controlled by the notion of information Importance. We present a simple and intuitive formulation of the summarization task as two components: a summary scoring function θ measuring how good a text is as a summary of the given sources, and an optimization technique O extracting a summary with a high score according to θ. This perspective offers interesting insights over previous summarization efforts and allows us to pinpoint promising research directions. In particular, we realize that previous works heavily constrained the summary scoring function in order to solve convenient optimization problems (e.g., Integer Linear Programming). We question this assumption and demonstrate that General Purpose Optimization (GPO) techniques like genetic algorithms are practical. These GPOs do not require mathematical properties from the objective function and, thus, the summary scoring function can be relieved from its previously imposed constraints. Additionally, the summary scoring function can be evaluated on its own based on its ability to correlate with humans. This offers a principled way of examining the inner workings of summarization systems and complements the traditional evaluations of the extracted summaries. In fact, evaluation metrics are also summary scoring functions which should correlate well with humans. Thus, the two main challenges of summarization, the evaluation and the development of summarizers, are unified within the same setup: discovering strong summary scoring functions. Hence, we investigated ways of uncovering such functions. First, we conducted an empirical study of learning the summary scoring function from data. The results show that an unconstrained summary scoring function is better able to correlate with humans. Furthermore, an unconstrained summary scoring function optimized approximately with GPO extracts better summaries than a constrained summary scoring function optimized exactly with, e.g., ILP. Along the way, we proposed techniques to leverage the small and biased human judgment datasets. Additionally, we released a new evaluation metric explicitly trained to maximize its correlation with humans. Second, we developed a theoretical formulation of the notion of Importance. In a framework rooted in information theory, we defined the quantities: Redundancy, Relevance and Informativeness. Importance arises as the notion unifying these concepts. More generally, Importance is the measure that guides which choices to make when information must be discarded. Finally, evaluation remains an open-problem with a massive impact on summarization progress. Thus, we conducted experiments on available human judgment datasets commonly used to compare evaluation metrics. We discovered that these datasets do not cover the high-quality range in which summarization systems and evaluation metrics operate. This motivates efforts to collect human judgments for high-scoring summaries as this would be necessary to settle the debate over which metric to use. This would also be greatly beneficial for improving summarization systems and metrics alike

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

El uso de material audiovisual en la enseñanza de los rechazos desde una perspectiva discursiva: una propuesta basada en la investigación

Refusals are complex face-threatening speech acts whose appropriate performance requires not only lengthy sequences of negotiation and cooperative achievements, but also face-saving strategies to accommodate the disruptive nature of the act (Gass & Houck 1999). Also, since they have a face threatening nature, they are subject to cultural variations. Consequently, care must be taken in the choice of refusal strategies. On that account, this paper first describes the speech act of refusal and reviews findings of empirical interventional studies on this speech act, with particular interest in understanding their methodological choices. Then, it presents the benefits of using audiovisual material for teaching pragmatics in a foreign or a second language instructional context. Finally, relying on excerpts from TV series, an instructional method for teaching refusals at the discourse level is presented. All the designed activities are built upon research-based recommendations for teaching refusals in hopes to provide teachers with resources and ideas for including pragmatics into their language courses.Los rechazos son actos de habla amenazadores cuyo uso apropiado requiere no sólo largas secuencias de negociación y logros de cooperación sino también estrategias para preservar la cara con el fn de acomodar la naturaleza disruptiva del acto (Gass & Houck 1999). Además, dado que tienen una naturaleza amenazadora, están sujetos a variaciones culturales. Consecuentemente, se debe tener cuidado en la elección de las estrategias del rechazo. En este sentido, este artículo describe en primer lugar el acto de habla del rechazo y revisa los resultados de estudios intervencionistas sobre este acto, con particular interés en comprender las opciones metodológicas que se siguen. A continuación, presenta los benefcios del uso de material audiovisual para la enseñanza de la pragmática en un contexto de enseñanza-aprendizaje de una lengua extranjera o segunda lengua. Finalmente, basándose en extractos de series de televisión, se presenta un método de instrucción para la enseñanza-aprendizaje de los rechazos a nivel discursivo. Las actividades diseñadas han seguido las recomendaciones de los estudios revisados con el fn de proporcionar al profesorado recursos e ideas didácticas para incorporar la pragmática en sus cursos de lengua

The inference of gene trees with species trees

Molecular phylogeny has focused mainly on improving models for the reconstruction of gene trees based on sequence alignments. Yet, most phylogeneticists seek to reveal the history of species. Although the histories of genes and species are tightly linked, they are seldom identical, because genes duplicate, are lost or horizontally transferred, and because alleles can co-exist in populations for periods that may span several speciation events. Building models describing the relationship between gene and species trees can thus improve the reconstruction of gene trees when a species tree is known, and vice-versa. Several approaches have been proposed to solve the problem in one direction or the other, but in general neither gene trees nor species trees are known. Only a few studies have attempted to jointly infer gene trees and species trees. In this article we review the various models that have been used to describe the relationship between gene trees and species trees. These models account for gene duplication and loss, transfer or incomplete lineage sorting. Some of them consider several types of events together, but none exists currently that considers the full repertoire of processes that generate gene trees along the species tree. Simulations as well as empirical studies on genomic data show that combining gene tree-species tree models with models of sequence evolution improves gene tree reconstruction. In turn, these better gene trees provide a better basis for studying genome evolution or reconstructing ancestral chromosomes and ancestral gene sequences. We predict that gene tree-species tree methods that can deal with genomic data sets will be instrumental to advancing our understanding of genomic evolution.Comment: Review article in relation to the "Mathematical and Computational Evolutionary Biology" conference, Montpellier, 201