The Final Exam, a recently released video game in which you play as a student caught amid a school shooting, lasts for around ten minutes, about the length of a real shooting event in a U.S. school. The game opens in an empty locker room. You hear distant gunfire, screams, harried footsteps, and the thudding of heavy furniture being overturned. The sense of disharmony is immediate: a familiar scene of youth and learning is grimly debased into one of peril. As the lockers surround you, their doors gaping, you feel caged: get me out of here. Moments later, as you enter the gymnasium, a two-minute countdown flashes on screen.

Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated ones using adversarial discriminators, leading to unstable training and mode-dropping issues. As reported by Zahee et al. (2017), even in the one-dimensional (1D) case, training a generative adversarial network (GAN) is challenging and often suboptimal. In this work, we develop a discriminator-free method for training one-dimensional (1D) generative implicit models and subsequently expand this method to accommodate multivariate cases. Our loss function is a discrepancy measure between a suitably chosen transformation of the model samples and a uniform distribution; hence, it is invariant with respect to the true distribution of the data. We first formulate our method for 1D random variables, providing an effective solution for approximate reparameterization of arbitrary complex distributions. Then, we consider the temporal setting (both univariate and multivariate), in which we model the conditional distribution of each sample given the history of the process. We demonstrate through numerical simulations that this new method yields promising results, successfully learning true distributions in a variety of scenarios and mitigating some of the well-known problems that state-of-the-art implicit methods present.

The "America's Got Talent" Season 12 contestants have certainly learned a thing or two since joining the hit NBC reality TV competition. A few days before the semifinals, some of the contestants shared one important lesson learned from their fellow contestants on the show. Dog act Sara & Hero has wowed the crowd since the judge cuts round. Despite not receiving rave reviews during their first audition, Sara & Hero have made it to the semifinals with flying colors. In the clip released by network, Sara jokingly says that she and her dog, Hero, learned how to become escape artists from Demian Aditya.

One of the biological side effects of being a human is the will to live. Luckily for us, one of the ways in which our brain gives the heads up to inform us of potentially dangerous situations is by invoking that little old survival instinct called "fear". Have you ever been stuck sitting next to someone in a cinema, completely unfazed by a horror movie, while you diverted your attention to the closest escape door? Everyone gets spooked by out by different stimuli – whether rational or irrational – clowns, gigantic spiders, or even marshmallows. Since we know that stimuli can evoke varying psychological responses, one group of researchers from our team at Data61 and MIT Media lab, set out to find what unites us in our phobia and terrifies us on a universal scale.

Robots could be used to help kids diagnosed with autism spectrum disorder (ASD), according to a new research project carried out jointly by a university and a robotics startup. Researchers at Spain's Universidad Miguel Hernández and the Spanish Aisoy Robotics company are collaborating to investigate how the latter group's pint-size educational Aisoy robot can enhance the effectiveness of therapy sessions at the UMH University Clinic -- particularly related to developing children's emotional, social, and cognitive skills. As an example, the robot will express emotions a child can then identify, or suggest playing certain games. Over the course of their time together, the idea is that kids will build up emotional attachments with the robot, and the interaction will aid with therapeutic adherence. "We already have an Emotional OS, called Airos, which includes an emotional engine, a cognitive engine, and a decision engine," José Manuel del Río, Aisoy's CEO, told Digital Trends.