The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.

An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to jointly predict the time of the event, and its rank in the cox partial log likelihood framework, our deep learning approach outperforms, in terms of survival time prediction quality and concordance index, other common methods for survival analysis, including the Cox Proportional Hazards model and a network trained on the cox partial log-likelihood.

Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in high-dimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry significantly improves performance on downstream tasks for several real-world public datasets.

Sören Schwertfeger finished his postdoctorate research on autonomous robots in Germany, and seemed set to go to Europe or the United States, where artificial intelligence was pioneered and established. Instead, he went to China. "You couldn't have started a lab like mine elsewhere," Mr. Schwertfeger said. The balance of power in technology is shifting. China, which for years watched enviously as the West invented the software and the chips powering today's digital age, has become a major player in artificial intelligence, what some think may be the most important technology of the future.

This question originally appeared on Quora. Capitalism works reasonably well as an economic system - provided that human beings are required to do effectively all of the labor. It worked far better than previous systems (slavery most notably). But capitalism has a long list of grievances, not the least of which is that human labor has slowly become devalued over time. The reason for that was simple: the assembly line, and the company that made its profits off of it, needed those workers.

In 1905, an Ohio farmer survived a railroad accident that cost him both of his legs. Two years later, he founded the Ohio Willow Wood company, using the namesake timber to hand-carve prosthetic limbs. The company grew, surviving the Great Depression and a fire that destroyed the plant, and still thrives today in rural Ohio. Few who work there now might remember the curious footnote in the company's history that occurred during World War II, when the rebuilt factory was diversified to build parts for PT boats and B-17 bombers. Today, it is ironic to consider a company that specializes in prosthetic limbs building parts for the war machine that unfortunately increases demand on companies making prosthetic limbs.

Inequality goes back to the Stone Age. Thirty thousand years ago, bands of hunter-gatherers in Russia buried some members in sumptuous graves replete with thousands of ivory beads, bracelets, jewels and art objects, while other members had to settle for a bare hole in the ground. Nevertheless, ancient hunter-gatherer groups were still more egalitarian than any subsequent human society, because they had very little property. Property is a pre-requisite for long-term inequality. Following the agricultural revolution, property multiplied and with it inequality.

Mutants like the X-Men might be much more likely to emerge in a human society living on another planet than here are on Earth, because radiation can really mess with evolution. Space agencies and private companies like Elon Musk's SpaceX are trying to make space travel more sustainable, learn about how the human body responds to long-term microgravity conditions and isolation and figure out how to grow crops in outer space, among other exercises -- all as people set their sights on places like Mars to explore and colonize. But what will happen to us once we get there? It's possible the first settlers will evolve into a new human species entirely, given enough time. On Earth, that amount of time is somewhere between thousands and millions of years, according to anthropologist John Hawks, a professor at the University of Wisconsin-Madison.

President Donald Trump's recent executive order calling for a sweeping review of the H-1B visa program has raised alarm in STEM-related industries that rely heavily on an international supply of high-skilled labor. Harrison Brody is a PhD student in neuroscience at Yale University and a member of Yale Science Diplomats. Current policy for H-1B visas, which permit highly skilled foreigners to work in the US temporarily, prohibits employers from undercutting wages or favoring foreign workers over Americans. But the president, along with a sizable bipartisan contingent, claims that the program has enabled private employers, especially those in the tech industry, to flood the labor market and provide temporary training for workers who eventually set up shop abroad. These grievances may be legitimate, but the conversation has largely ignored another industry that depends on the H-1B visa program: academic scientific research.

Humanoid robots are more than just sci-fi novelties in South Korea, which holds the top spot for the world's highest robot density – beating out China, Japan, Germany and the United States. Robot density refers to the ratio of industrial robots to employees. To maintain its lead, the South Korean government has committed $450 million to robotics over the next five years. 'The Highs' is a series which looks at countries that rank the highest in certain categories – for better or worse.