Normative data for idiomatic expressions

Peer reviewedPublisher PD

Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data

We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the proposed approach -- the semi-supervised, denoising adversarial autoencoder -- is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. We analyse the contributions of both the adversarial and denoising components of the model and find that the combination yields superior classification performance in the setting of limited labelled training data.Comment: Under consideration for the IET Computer Vision Journal special issue on "Computer Vision in Cancer Data Analysis

The Analysis of Two Esl/efl Websites: Englishclub and Activities for Esl Students

This study reviewed two well known ESL/EFL websites namely EnglishClub and Activities for ESL Students using the website evaluation framework proposed by Hasan and Abuelrub (2011). The writers found that Activities for ESL Students met 79.92% of the website evaluation criteria with 211 of the total score; while EnglishClub met 79.54% of the website evaluation criteria with 210 of the total score. Thus there was no significant difference between these two websites. Both EnglishClub and Activities for ESL Students are good for ESL/EFL learner

Costly capital reallocation and enery use

In time series data, energy use does not change much with energy price changes. However, energy use is responsive to international differences in energy prices in cross-section data across countries. In this paper we consider a model of energy use in which production takes place at individual plants and capital can be used either to directly produce output or to reduce the energy required to run the plant. We assume that reallocating capital from one use to another is costly. This turns out to be crucial for the quantitative properties of the model to be in conformity with the low short-run and high long-run elasticities of energy use seen in data

Individual Differences in Performance Speed Are Associated With a Positivity/Negativity Bias. An ERP and Behavioral Study

There is a current dispute over the origins, incidence, and development of Positivity Bias, i.e., preferential processing of positive relative to negative information. We addressed this question using a multi-method technique of behavioral, psychometric and event-related potential (ERP) measures in a lexical decision task (LDT). Twenty-four university students (11 female) participated (age range 18–26), but four were omitted owing to data issues. Participants were classified as Positivity Biased (PB) if their LDT responses to positive words were faster than negative words, and vice versa for those classified as Negativity Biased (NB), leading to a group of 11 PB participants and a group of 9 NB participants. Interestingly, the PB group was significantly faster overall than the NB group and had significantly shorter P2 component ERP latencies in the left occipital region. Furthermore, the PB group had significantly higher scores for expressive suppression (ES), together with higher scores for Crystallized Knowledge and for cognitive reappraisal (CR). These results suggest that around 55% of the students had Positivity Bias, and these were more efficient in processing information and had better emotion regulation abilities than those with a Negativity Bias

Costly capital reallocation and energy use

In time series data, energy use does not change much with energy price changes. However, energy use is responsive to international differences in energy prices in cross-section data across countries. In this paper we consider a model of energy use in which production takes place at individual plants and capital can be used either to directly produce output or to reduce the energy required to run the planto We assume that reallocating capital from one use to another is costly. This turns out to be crucial for the quantitative properties of the model to be in conformity with the low short-mn and high long-run elasticities of energy use seen in data. Furthermore, our model displays variations in capacity utilization that are in line with those observed during the period of major oil price increases.Energy price, Energy use, Costly capital reallocation, Number of plants.

COSTLY CAPITAL REALLOCATION AND ENERY USE

In time series data, energy use does not change much with energy price changes. However, energy use is responsive to international differences in energy prices in cross-section data across countries. In this paper we consider a model of energy use in which production takes place at individual plants and capital can be used either to directly produce output or to reduce the energy required to run the plant. We assume that reallocating capital from one use to another is costly. This turns out to be crucial for the quantitative properties of the model to be in conformity with the low short-run and high long-run elasticities of energy use seen in data.Energy Price; Energy Use; Costly Capital Reallocation