He previously shared a stunning timelapse of what it's like to fly over Earth at speeds unimaginable to the average person. The video shows an incredible view of the trip over Alaska to the Andes in 260 seconds. The crews aboard the space station frequently share updates on their life hundreds of miles above the surface, showing what it's like to live and work in orbit for months on end. In the past, they've revealed stunning views of everything from auroras to moon-sets. Earlier this month, Russian cosmonaut Oleg Artemyev released a video of the narrow tunnels astronauts must traverse to navigate the ISS.

MOSCOW – Russia's space agency says that one of the International Space Station's computers has malfunctioned, but the glitch doesn't pose any risks to the crew. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed. It said Russian flight controllers plan to reboot it Thursday. The agency emphasized that the computer problem wouldn't affect the station's crew -- NASA's Serena Aunon-Chancellor, Russian Sergei Prokopyev and German Alexander Gerst. It said two other computers can maintain the station's operation.

We describe algorithms for use by prediction markets in forming a crowd consensus joint probability distribution over thousands of related events. Equivalently, we describe market mechanisms to efficiently crowdsource both structure and parameters of a Bayesian network. Prediction markets are among the most accurate methods to combine forecasts; forecasters form a consensus probability distribution by trading contingent securities. A combinatorial prediction market forms a consensus joint distribution over many related events by allowing conditional trades or trades on Boolean combinations of events. Explicitly representing the joint distribution is infeasible, but standard inference algorithms for graphical probability models render it tractable for large numbers of base events. We show how to adapt these algorithms to compute expected assets conditional on a prospective trade, and to find the conditional state where a trader has minimum assets, allowing full asset reuse. We compare the performance of three algorithms: the straightforward algorithm from the DAGGRE (Decomposition-Based Aggregation) prediction market for geopolitical events, the simple block-merge model from the SciCast market for science and technology forecasting, and a more sophisticated algorithm we developed for future markets.

We study the convergence of a variant of distributed gradient descent (DGD) on a distributed low-rank matrix approximation problem wherein some optimization variables are used for consensus (as in classical DGD) and some optimization variables appear only locally at a single node in the network. We term the resulting algorithm DGD+LOCAL. Using algorithmic connections to gradient descent and geometric connections to the well-behaved landscape of the centralized low-rank matrix approximation problem, we identify sufficient conditions where DGD+LOCAL is guaranteed to converge with exact consensus to a global minimizer of the original centralized problem. For the distributed low-rank matrix approximation problem, these guarantees are stronger---in terms of consensus and optimality---than what appear in the literature for classical DGD and more general problems.

A configuration of training refers to the combinations of feature engineering, learner, and its associated hyperparameters. Given a set of configurations and a large dataset randomly split into training and testing set, we study how to efficiently identify the best configuration with approximately the highest testing accuracy when trained from the training set. To guarantee small accuracy loss, we develop a solution using confidence interval (CI)-based progressive sampling and pruning strategy. Compared to using full data to find the exact best configuration, our solution achieves more than two orders of magnitude speedup, while the returned top configuration has identical or close test accuracy.

Bribery in election (or computational social choice in general) is an important problem that has received a considerable amount of attention. In the classic bribery problem, the briber (or attacker) bribes some voters in attempting to make the briber's designated candidate win an election. In this paper, we introduce a novel variant of the bribery problem, "Election with Bribed Voter Uncertainty" or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted. This uncertainty occurs either because a bribed voter may not cast its vote in fear of being caught, or because a bribed voter is indeed caught and therefore its vote is discarded. As a first step towards ultimately understanding and addressing this important problem, we show that it does not admit any multiplicative $O(1)$-approximation algorithm modulo standard complexity assumptions. We further show that there is an approximation algorithm that returns a solution with an additive-$\epsilon$ error in FPT time for any fixed $\epsilon$.

Researchers from Florida Atlantic University's College of Engineering and Computer Science published a study in Health Information Science and Systems that shows how machine learning and advanced analytics could lead to Medicare fraud detection. The breakthrough could lead to $19-65 billion annual savings of Medicare funds lost to fraud. The researchers tested six different machine learners on both balanced and imbalanced data sets using Medicare Part B data, ultimately finding the RF100 random forest algorithm to be the most effective in detecting potential fraudulent claims, and that imbalanced data sets provided the most accurate results. The research team used four years worth of Medicare Part B data totaling 37 million cases and examined them for potential patient abuse, neglect, and overcharging or charging for services that were never provided. They used the NPI -- National Provider Identifier, which is a unique identification number issued to healthcare providers by the government -- to match fraud labels to the data, checking against provider details, payment and charges, procedure codes, total procedures performed, and medical specialty.

In the past few years, artificial intelligence has advanced so quickly that it now seems that hardly a month goes by without a newsworthy AI breakthrough. In areas as wide-ranging as speech translation, medical diagnosis and game play, we have seen computers outperform humans in startling ways. This has sparked a discussion about what impact AI will have on employment. Some fear that as AI improves, it will supplant workers in the job force, creating an ever-growing pool of unemployable humans who cannot economically compete with machines in any meaningful way. This concern, while understandable, is unfounded.

How Americans vote today will determine who sits in government for at least the next two years--but tonight's outcome will shape policy for years to come. With hundreds of key races nationwide, some of which are poised to significantly shift long-established state and national leadership, the 2018 midterm elections will be one to watch. Here's how you can stay informed before, during, and after tonight's midterm elections. First: Have you voted yet? If you're heading to the polls, consider a few of these resources to help you make sense of the salient issues of this year, and find more information about voting.

The agency emphasized that the computer problem wouldn't affect the station's crew -- NASA's Serena Aunon-Chancellor, Russian Sergei Prokopyev and German Alexander Gerst. It said two other computers can maintain the station's operation.