The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
Fifty years ago, the first industrial robot arm (called Unimate) assembled a simple breakfast of toast, coffee, and champagne. While it might have looked like a seamless feat, every movement and placement was coded with careful consideration. Even with today's more intelligent and adaptive robots, this task remains difficult for machines with rigid hands. They tend to work only in structured environments with predefined shapes and locations, and typically can't cope with uncertainties in placement or form. In recent years, though, roboticists have come to grips with this problem by making fingers out of soft, flexible materials like rubber.
We propose a novel neural topic model in the Wasserstein autoencoders (W AE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.
The outrage and shots being taken at Pepsi and Kendall Jenner for "that" commercial have mostly subsided, but there's still some some venom left in the failed pop culture moment, and it's packed with brand-melting shade. Someone took the Pepsi commercial and mixed it with the famous glasses effect from the 1988 science fiction classic They Live, to reveal the ugly truth behind the now infamous sugar water ad. SEE ALSO: Here are the funniest reactions to Kendall Jenner's terrible'woke' Pepsi ad In They Live, characters use special glasses to reveal hidden consumer advertisement messages and the evil alien robots promoting them from behind false faces. As usual with ugly truths, not everyone's ready for it, and some have to be forced to use the #WokeTech provided by the glasses. The Pepsi video remix is being shared all over social media, but the only clue as to the origin of the brilliant short is a YouTube account that features even more They Live treatments, featuring the likes of Donald Trump, Hillary Clinton and one of the cast members from Shark Tank.
Is drinking wine better for you than eating broccoli? You may want to put down that glass of wine or pint of beer. Despite claims in recent years that a glass of red wine could reduce chances of getting heart disease, new research is discovering that the information may be faulty. Scientists are now saying that many of the studies touting alcohol's benefits were actually subsidized by alcohol companies, reports Wired. The real science behind these so-called benefits may have been grossly exaggerated and may have lead to the systemic burying of a serious risk of imbibing too much alcohol-- cancer.