In this paper we propose a primal-dual proximal extragradient algorithm to solve the generalized Dantzig selector (GDS) estimation problem, based on a new convex-concave saddle-point (SP) reformulation. Our new formulation makes it possible to adopt recent developments in saddle-point optimization, to achieve the optimal $O(1/k)$ rate of convergence. Compared to the optimal non-SP algorithms, ours do not require specification of sensitive parameters that affect algorithm performance or solution quality. We also provide a new analysis showing a possibility of local acceleration to achieve the rate of $O(1/k^2)$ in special cases even without strong convexity or strong smoothness. As an application, we propose a GDS equipped with the ordered $\ell_1$-norm, showing its false discovery rate control properties in variable selection. Algorithm performance is compared between ours and other alternatives, including the linearized ADMM, Nesterov's smoothing, Nemirovski's mirror-prox, and the accelerated hybrid proximal extragradient techniques.

Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on past payoff observations that are subject to noise and random disturbances. First, in the single-player case (corresponding to an agent trying to adapt to an arbitrarily changing environment), we show that the stochastic dynamics under study lead to no regret almost surely, irrespective of the noise level in the player's observations. In the multi-player case, we find that dominated strategies become extinct and we show that strict Nash equilibria are stochastically stable and attracting; conversely, if a state is stable or attracting with positive probability, then it is a Nash equilibrium. Finally, we provide an averaging principle for 2-player games, and we show that in zero-sum games with an interior equilibrium, time averages converge to Nash equilibrium for any noise level. Contents 1. Introduction 2 2. The model 5 3. Regret minimization 11 4. Extinction of dominated strategies 14 5.

In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting - that is, providing a probability distribution rather than a simple point forecast - and incorporate it into a web based API that provides quick and easy access to reliable forecasts.

A French ship searching the Mediterranean has detected black box signals from a missing EgyptAir flight in the waters between the Greek island of Crete and the Egyptian coast, a development that could help solve the mystery of why the aircraft crashed into the sea last month, killing all 66 on board. The discovery, announced Wednesday, could help guide search teams to the wreckage and the flight's data and cockpit voice recorders, which if retrieved unharmed could reveal whether a mechanical fault or a hijacking or bomb caused the disaster. In the two weeks since Flight 804 disappeared from radar en route to Cairo from Paris, only small pieces of debris and human remains have been retrieved from the crash site. No terrorist group has claimed responsibility, though Egypt's civil aviation minister, Sherif Fathi, has said terrorism is a more likely cause than equipment failure or some other catastrophic event. The flight recorders will be critical to determining whether the disaster was caused by an accident or a deliberate act.

Your parents are watching the Berlin Wall being torn down, but you're upstairs in your room, fresh batteries in your Casio SA-1, prepping for your concert. The built-in beats are hot. You've been listening to the "Wake me up before you go-go" demo song and you think you've been inspired. You select instrument number 02, "HONKY-TONK PIANO." It's time to tickle the plastic ivories and burn this mother down.

Here's a hard fact that will be hard for many to accept: Yes, a robot will probably be able to do your job someday. Remember the days when you had to go to a cashier at the grocery store and you didn't have to bag your own groceries? The same thing happening in your local grocery store is also happening in factories, and even offices all over the world. And if proponents of self-driving cars have their way, it won't be long before you never again need a human to give you a ride home from the airport. But who will hold the power in this brave, new, automated world?

We're happy to announce Magenta, a project from the Google Brain team that asks: Can we use machine learning to create compelling art and music? Soon we'll begin accepting code contributions from the community at large. If you'd like to keep up on Magenta as it grows, you can follow us on our GitHub and join our discussion group. First, it's a research project to advance the state of the art in machine intelligence for music and art generation. Machine learning has already been used extensively to understand content, as in speech recognition or translation.

"If we can do it in the right and thoughtful way we are always open to it," said Pichai at the Code conference here. "I care about serving consumers everywhere." Google pulled out of mainland China and moved its Chinese-language search engine to Hong Kong in 2010 after a series of cyber attacks on Google originated in the country. Google also said it would stop censoring search results in China. The controversial move cut Google off from the fast-growing Chinese market, one that's been courted by rival Facebook and constitutes the second-biggest market for Apple.

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE. Killer robots pose a threat to all of us. In the movies, this threat is usually personified as an evil machine bent on destroying humanity for reasons of its own. In reality, the threat comes from within us.

In-home robots to help with everything from cooking and cleaning to childcare and companionship have been the stuff of sci-fi for decades. But, with the unveiling this week of the Wall-E-like Asus Zenbo, this is the year that science-fiction could turn into science-fact, introducing home assistants that can talk to you, control your electronics and help out with the chores. Here are five that could be knocking at your door in the near future. The latest robot to offer help around the home is Asus's Zenbo, which, at 599 ( 410), costs about the same as a smartphone. Designed to be a do-it-all smarthome assistant, Zenbo can drive itself around your place, using various cameras to keep it from bumping into the walls.