Temporally unfolding neural representation of pictorial occlusion.

The Representation of Iran in Englishcentral Educational Website: Unfolding the Hidden Curriculum

Despite their widespread popularity and rapid growth, the Internet-mediated English educational materials for learners of English as a foreign/second language (FL/SL) have rarely been analyzed in terms of their potential hidden curriculum. Accordingly, the present study aims to address this need through conducting a CDA investigation into some lessons which are randomly selected from an English...

Gender Representation in Interchange (Third Edition) Series: A Social Semiotics Analysis

Gender representation has long been studied in both verbal and visual modes of ELT textbooks. However, regarding the visual mode, research has mainly focused on superficial analyses of how often each gender appears in different roles rather than on how the two genders are represented. The tools proposed in Kress and van Leeuwen’s (2006) social semiotics framework, however, permit deep ana...

Neuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

Decoding the representation of numerical values from brain activation patterns.

Human neuroimaging studies have increasingly converged on the possibility that the neural representation of specific numbers may be decodable from brain activity, particularly in parietal cortex. Multivariate machine learning techniques have recently demonstrated that the neural representation of individual concrete nouns can be decoded from fMRI patterns, and that some patterns are general ove...

Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network