Commission des Communautes Europeennes: Groupe du Porte-Parole = Commission of European Communities: Spokesman Group. Spokesman Service Note to National Offices Bio No. (81) 276, 8 July 1981

This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse – but still acceptable – performance when compared to the single language model, while benefiting from better generalization properties across languages

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

Learning Design for Children and Youth in Makerspaces: Methodical-Didactical Variations of Maker Education Activities Concerning Learner’s Interest, Learning with Others and Task Description

For some years now, “maker education” has been conquering the world, and with extensive literature describing projects and activities as well as their characteristics and effects. Many authors have described principles of maker education such as working on a product and do-it-yourself activities. However, the literature on how to develop and design a maker activity with children is still limited. This would be of interest to and inform the systematic training of teachers and maker educators. In this paper we propose an overview of the methodological-didactical variations in maker education base on the systematic analysis of the original principles of adults learning in makerspaces to extrapolate the principles for working with children in maker education. Therefore, this paper offers a collection of methodological-didactical variations concerning three aspects, namely (a) the inclusion of the learner’s own interests, (b) learning from and with others, and (c) the kinds of task available at hand. In this way it is intended to offer practitioners support for the design and development of their own maker education programs.falseParis, Franc

Hazard Assessment for Manufacture of Combustible Cartridge Cases using Picrite

A systematic study of the effect of impact, friction, flame and electric spark sensitivity was carried out on the samples combustible cartridge case (CCC) withdrawn at different stages of manufacture. These are Stage I dried felted CCC; stage II-CCC from stage III Coated with nitrocellulose coating. based on the results obtained from various experiments, the CCC can be classified for handling storage and transportation as Group 3, for safety distance category as UN 1.3 and for fire fighting as class 2. further it is concluded from hazard analysis study that the CCCs are safe to handle but these should be protected from naked flame

The Effects of Vision Impairment on Balance in Athletes and Non-Athletes

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Basal ganglia correlates of fatigue in young adults

Although the prevalence of chronic fatigue is approximately 20% in healthy individuals, there are no studies of brain structure that elucidate the neural correlates of fatigue outside of clinical subjects. We hypothesized that fatigue without evidence of disease might be related to changes in the basal ganglia and prefrontal cortex and be implicated in fatigue with disease. We aimed to identify the white matter structures of fatigue in young subjects without disease using magnetic resonance imaging (MRI). Healthy young adults (n = 883; 489 males and 394 females) were recruited. As expected, the degrees of fatigue and motivation were associated with larger mean diffusivity (MD) in the right putamen, pallidus and caudate. Furthermore, the degree of physical activity was associated with a larger MD only in the right putamen. Accordingly, motivation was the best candidate for widespread basal ganglia, whereas physical activity might be the best candidate for the putamen. A plausible mechanism of fatigue may involve abnormal function of the motor system, as well as areas of the dopaminergic system in the basal ganglia that are associated with motivation and reward

Increased Executive Functioning, Attention, and Cortical Thickness in White-Collar Criminals

Very little is known on white collar crime and how it differs to other forms of offending. This study tests the hypothesis that white collar criminals have better executive functioning, enhanced information processing, and structural brain superiorities compared to offender controls. Using a case-control design, executive functioning, orienting, and cortical thickness was assessed in 21 white collar criminals matched with 21 controls on age, gender, ethnicity, and general level of criminal offending. White collar criminals had significantly better executive functioning, increased electrodermal orienting, increased arousal, and increased cortical gray matter thickness in the ventromedial prefrontal cortex, inferior frontal gyrus, somatosensory cortex, and the temporal-parietal junction compared to controls. Results, while initial, constitute the first findings on neurobiological characteristics of white-collar criminals It is hypothesized that white collar criminals have information-processing and brain superiorities that give them an advantage in perpetrating criminal offenses in occupational settings

Effects of the neurogranin variant rs12807809 on thalamocortical morphology in schizophrenia

10.1371/journal.pone.0085603PLoS ONE812-POLN