Simultaneous interpreting : walking a tightrope

Several phenomena associated with the differences in the performance of novice interpreters and semi-professionals have been discussed in the paper. Particular emphasis was placed on the occurrence of imported cognitive load which strongly influenced the performance of the subjects also in places where no intrinsic difficulty had been detected. Nevertheless, too little evidence was provided to establish a more detailed pattern of imported cognitive load, which was due to the limited number of participants in the study. It would be possible to obtain more detailed data and comments from the participants by means of interviews conducted individually with the participants. It would allow asking detailed questions to the participants, which might be a more reliable method than the immediate retrospective accounts. Moreover, in the present study such variables as gender differences, age differences and the possible influence of other foreign languages were not taken into account. Perhaps these variables might shed some light on the issue of the management of cognitive resources. Also, the corpus gathered for the present study may be used for the investigation of other aspects of the SI performance

El uso de estrategias de comunicación de aprendices de inglés como L2 con diferentes niveles de competencia en un contexto oral interactivo

Indexación: Web of Science; Scielo.Abstract: This study aims to examine the different communication strategies (CSs) EFL learners employ when communicating orally, and determine the relationship between the learners’ proficiency level and their CS use. Spoken data from three conversations held by Spanish learners of English of different levels were analysed in order to determine the type of CSs they used when interacting with a native speaker (NS) in an informal environment outside the classroom. The identification of the CSs was carried out following Dörnyei and Körmos’ taxonomy (1998). Overall results show that there is an association between the learners’ proficiency level and their CS usage. Results from a detailed analysis confirmed this relationship and revealed that the learners’ linguistic competence is not only related to the frequency of the CSs used but mostly to the type of CS.Resumen: El objetivo de este estudio fue analizar las diferentes estrategias de comunicación (EsC) que utilizan aprendices de inglés como L2 al comunicarse oralmente, y determinar la relación entre la competencia lingüística de estos estudiantes y el uso de las EsC. Se analizó un corpus oral obtenido de tres conversaciones entre estudiantes de inglés de distintos niveles de competencia con el propósito de descubrir el tipo de EsC que éstos utilizan al interactuar con un hablante nativo en un ambiente informal fuera del aula. La identificación de las EsC utilizadas se realizó mediante la taxonomía propuesta por Dörnyei y Körmos (1998). Desde un punto de vista general los resultados muestran que existe una asociación entre el nivel de competencia lingüística de los estudiantes y las EsC que éstos utilizan. Esto fue corroborado mediante un análisis más detallado de los datos que demostró una relación no sólo entre el nivel de competencia y la frecuencia de uso de las EsC sino principalmente con el tipo de estrategias utilizadas por cada nivel.http://www.scielo.cl/scielo.php?pid=S0718-09342016000100004&script=sci_abstrac

The prodrome of autism: early behavioral and biological signs, regression, peri- and post-natal development and genetics

Autism is one of the most heritable neurodevelopmental conditions and has an early onset, with symptoms being required to be present in the first 3 years of life in order to meet criteria for the ‘core’ disorder in the classification systems. As such, the focus on identifying a prodrome over the past 20 years has been on pre-clinical signs or indicators that will be present very early in life, certainly in infancy. A number of novel lines of investigation have been used to this end, including retrospective coding of home videos, prospective population screening and ‘high risk’ sibling studies; as well as the investigation of pre- and peri-natal, brain developmental and other biological factors. Whilst no single prodromal sign is expected to be present in all cases, a picture is emerging of indicative prodromal signs in infancy and initial studies are being undertaken to attempt to ameliorate the early presentation and even ‘prevent’ emergence of the full syndrome

Are language production problems apparent in adults who no longer meet diagnostic criteria for attention-deficit/hyperactivity disorder?

In this study, we examined sentence production in a sample of adults (N = 21) who had had attention-deficit/hyperactivity disorder (ADHD) as children, but as adults no longer met DSM-IV diagnostic criteria (APA, 2000). This “remitted” group was assessed on a sentence production task. On each trial, participants saw two objects and a verb. Their task was to construct a sentence using the objects as arguments of the verb. Results showed more ungrammatical and disfluent utterances with one particular type of verb (i.e., participle). In a second set of analyses, we compared the remitted group to both control participants and a “persistent” group, who had ADHD as children and as adults. Results showed that remitters were more likely to produce ungrammatical utterances and to make repair disfluencies compared to controls, and they patterned more similarly to ADHD participants. Conclusions focus on language output in remitted ADHD, and the role of executive functions in language production

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video

Language and social/emotional problems identified at a universal developmental assessment at 30 months

Non peer reviewedPublisher PD

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2