407 research outputs found
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
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
In recent years, heatmap regression based models have shown their
effectiveness in face alignment and pose estimation. However, Conventional
Heatmap Regression (CHR) is not accurate nor stable when dealing with
high-resolution facial videos, since it finds the maximum activated location in
heatmaps which are generated from rounding coordinates, and thus leads to
quantization errors when scaling back to the original high-resolution space. In
this paper, we propose a Fractional Heatmap Regression (FHR) for
high-resolution video-based face alignment. The proposed FHR can accurately
estimate the fractional part according to the 2D Gaussian function by sampling
three points in heatmaps. To further stabilize the landmarks among continuous
video frames while maintaining the precise at the same time, we propose a novel
stabilization loss that contains two terms to address time delay and non-smooth
issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets
clearly demonstrate that the proposed method is more accurate and stable than
the state-of-the-art models.Comment: Accepted to AAAI 2019. 8 pages, 7 figure
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%50.1%) and on two future unseen rounds of
human generated attacks (32.5%43.4%, and 29.4%40.2%). In hate
speech detection, we see AUC gains on current attacks (0.76 0.84) and a
future round (0.77 0.79). Attacks from methods that do not learn the
distribution of existing human adversaries, meanwhile, degrade robustness
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