Tunability of the elastocaloric response in main-chain liquid crystalline elastomers

Materials exhibiting a large caloric effect could lead to the development of a new generation of heat-management technologies that will have better energy efficiency and be potentially more environmentally friendly. The focus of caloric materials investigations has shifted recently from solid-state materials towards soft materials, such as liquid crystals and liquid crystalline elastomers. It has been shown recently that a large electrocaloric effect exceeding 6 K can be observed in smectic liquid crystals. Here, we report on a significant elastocaloric response observed by direct elastocaloric measurements in main-chain liquid crystal elastomers. It is demonstrated that the character of the nematic to paranematic/isotropic transition can be tuned from the supercritical regime towards the first-order regime, by decreasing the density of crosslinkers. In the latter case, the latent heat additionally enhances the elastocaloric response. Our results indicate that a significant elastocaloric response is present in main-chain liquid crystalline elastomers, driven by stress fields much smaller than in solid elastocaloric materials. Therefore, elastocaloric soft materials can potentially play a significant role as active cooling/heating elements in the development of new heat-management devices

The Effect of Class Noise on Continuous Test Case Selection: A Controlled Experiment on Industrial Data

Continuous integration and testing produce a large amount of data about defects in code revisions, which can be utilized for training a predictive learner to effectively select a subset of test suites. One challenge in using predictive learners lies in the noise that comes in the training data, which often leads to a decrease in classification performances. This study examines the impact of one type of noise, called class noise, on a learner’s ability for selecting test cases. Understanding the impact of class noise on the performance of a learner for test case selection would assist testers decide on the appropriateness of different noise handling strategies. For this purpose, we design and implement a controlled experiment using an industrial data-set to measure the impact of class noise at six different levels on the predictive performance of a learner. We measure the learning performance using the Precision, Recall, F-score, and Mathew Correlation Coefficient (MCC) metrics. The results show a statistically significant relationship between class noise and the learners performance for test case selection. Particularly, a significant difference between the three performance measures (Precision, F-score, and MCC)under all the six noise levels and at 0% level was found, whereas a similar relationship between recall and class noise was found at a level above30%. We conclude that higher class noise ratios lead to missing out more tests in the predicted subset of test suite and increases the rate of false alarms when the class noise ratio exceeds 30

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

"Come Together!": Interactions of Language Networks and Multilingual Communities on Twitter

Emerging tools and methodologies are providing insight into the factors that promote the propagation of information in online social networks following significant activities, such as high-profile international social or societal events. This paper presents an extensible approach for analysing how different language communities engage and interact on the social networking platform Twitter via an analysis of the Eurovision Song Contest held in Stockholm, Sweden, in May 2016. By utilising language information from user profiles (N=1,226,959) and status updates (N=7,926,746) to identify and categorise communities, our approach is able to categorise these interactions, as well as construct network graphs to provide further insight on these multilingual communities. The results show that multilingualism is positively correlated with activity whilst negatively correlated with posting in the user’s own language

Toward a better understanding of emotional dynamics on Facebook

On online social media users tend to aggregate in echo chambers, where they shape and reinforce their worldview by discussing and interacting with like-minded people. Such a scenario fosters misinformation spreading, which may influence public opinion. To determine the main factors behind narratives’ emergence, characterizing polarization dynamics and users’ emotional response to social contents is, thus, crucial. In this paper, we address such a challenge by looking at two different and contrasting narratives, science and conspiracy. We introduce a new metric, the bipolarity, and show how it can help in finding non-trivial proxies of the debate’s polarization. Our approach may provide interesting insights for a better understanding of both emotional and polarization dynamics on online social media

Sentiment of Emojis

There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar