An exploration of the role of narrative skills in higher-level reading comprehension of teenagers with hearing loss

This Study examines the relationship between scores on adolescents’ self-generated narratives and standardized reading-comprehension scores. This relationship is also compared with the more simple language metrics: vocabulary and syntax.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Report of a Consultative Forum: Higher-Level Human Resource Development Strategies in the Caribbean Subregion of the 1990s

Includes bibliography

The interphotoreceptor retinoid binding protein gene in therian mammals: Implications for higher level relationships and evidence for loss of function in the marsupial mole

The subclass Theria of Mammalia includes marsupials (infraclass Metatheria) and placentals (infraclass Eutheria). Within each group, interordinal relationships remain unclear. One limitation of many studies is incomplete ordinal representation. Here, we analyze DNA sequences for part of exon 1 of the interphotoreceptor retinoid binding protein gene, including 10 that are newly reported, for representatives of all therian orders. Among placentals, the most robust clades are Cetartiodactyla, Paenungulata, and an expanded African clade that includes paenungulates, tubulidentates, and macroscelideans. Anagalida, Archonta, Altungulata, Hyracoidea + Perissodactyla, Ungulata, and the “flying primate” hypothesis are rejected by statistical tests. Among marsupials, the most robust clade includes all orders except Didelphimorphia. The phylogenetic placement of the monito del monte and the marsupial mole remains unclear. However, the marsupial mole sequence contains three frameshift indels and numerous stop codons in all three reading frames. Given that the interphotoreceptor retinoid binding protein gene is a single-copy gene that functions in the visual cycle and that the marsupial mole is blind with degenerate eyes, this finding suggests that phenotypic degeneration of the eyes is accompanied by parallel changes at the molecular level as a result of relaxed selective constraints.

Transportation of higher-level language programs: exemplified by an ALGOL 68 transportation representation /

Includes bibliographical references.

Higher medical education, the true interest of the public and the profession : Two addresses delivered before the Medical department of the University of Pennsylvania on October 1, 1877, and October 2, 1893 /

Mode of access: Internet.

Education to careers Illinois : the role of middle-level education.

"4/98/15M"--Colophon.

Higher-level language deficits resulting from left primary cerebellar lesions

Background: Contemporary neuropsychological studies suggest that cerebellar lesions may impact upon higher-level cognitive functioning via mechanisms of crossed cerebello-cerebral diaschisis. Accordingly, right cerebellar lesions have been previously associated with linguistic impairments such as reduced word fluency and agrammatic output. Recently, however, neuroimaging investigations have also identified ipsilateral cerebral hypoperfusion as a consequence of cerebellar lesions, implicating a potential role for the left cerebellum in the mediation of language processes. Aims: The purpose of this research was to investigate the effects of left cerebellar lesions of vascular origin, on general as well as high-level language skills. Methods & Procedures: Linguistic profiles were compiled for five individuals with left primary cerebellar lesions utilising a comprehensive language test battery. Individual scores relevant to each subtest were compared to a group of non-neurologically impaired controls. The criterion for anomalous performance was established as greater than or equal to 1.5 SD below the mean of the control group. Outcomes & Results: The findings of this research suggest that higher-level language deficits may result from left primary cerebellar lesions. All participants demonstrated deficits on measures of word fluency, sentence construction within a set context, producing word definitions, and producing multiple definitions for the same word. Deficits were also noted for several participants on measures of understanding figurative language, forming word associations, identifying and correcting semantic absurdities, and producing synonyms and antonyms. Conclusions: The results presented challenge the notion of a lateralised linguistic cerebellum, supporting a potential role for the left as well as right cerebellar hemispheres in the regulation of language processes, presumably via cerebellar-basal ganglia/thalamo-cortical pathways.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.