Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed two models: (i) a feature-based model that leverages peoples' exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work

Effect of Zr modification on solidification behavior and mechanical properties of Mg–Y–RE (WE54) alloy

AbstractMagnesium alloys containing rare earth elements (RE) have received considerable attention in recent years due to their high mechanical strength and good heat-resisting performance. Among them, Mg–5%Y–4%RE (WE54) magnesium alloy is a high strength sand casting magnesium alloy for use at temperatures up to 300 °C, which is of great interest to engineers in the aerospace industry. In the present work, the solidification behavior of Zr-containing WE54 alloy and Zr-free alloy was investigated by computer-aided cooling curve analysis (CA-CCA) technique. And the solidification microstructure and mechanical properties of them were also investigated comparatively. It is found from the cooling curves and as-cast microstructure of WE54 alloy that the nucleation temperature of α-Mg in WE54 alloy increases after Zr addition, and the as-cast microstructure of the alloy is significantly refined by Zr. While the phase constitution of WE54 alloy is not changed after Zr addition. These phenomena indicate that Zr acts as heterogeneous nuclei during the solidification of WE54 alloy. Due to refined microstructure, the mechanical properties of Zr-containing WE54 alloy is much higher than Zr-free WE54 alloy

Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks

Real-world natural language processing systems need to be robust to human adversaries. Collecting examples of human adversaries for training is an effective but expensive solution. On the other hand, training on synthetic attacks with small perturbations - such as word-substitution - does not actually improve robustness to human adversaries. In this paper, we propose an adversarial training framework that uses limited human adversarial examples to generate more useful adversarial examples at scale. We demonstrate the advantages of this system on the ANLI and hate speech detection benchmark datasets - both collected via an iterative, adversarial human-and-model-in-the-loop procedure. Compared to training only on observed human attacks, also training on our synthetic adversarial examples improves model robustness to future rounds. In ANLI, we see accuracy gains on the current set of attacks (44.1%$\,\to\,$50.1%) and on two future unseen rounds of human generated attacks (32.5%$\,\to\,$43.4%, and 29.4%$\,\to\,$40.2%). In hate speech detection, we see AUC gains on current attacks (0.76 $\to$ 0.84) and a future round (0.77 $\to$ 0.79). Attacks from methods that do not learn the distribution of existing human adversaries, meanwhile, degrade robustness