We derive fundamental sample complexity bounds for recovering sparse and structured signals for linear and nonlinear observation models including sparse regression, group testing, multivariate regression and problems with missing features. In general, sparse signal processing problems can be characterized in terms of the following Markovian property. We are given a set of $N$ variables $X_1,X_2,\ldots,X_N$, and there is an unknown subset of variables $S \subset \{1,\ldots,N\}$ that are relevant for predicting outcomes $Y$. More specifically, when $Y$ is conditioned on $\{X_n\}_{n\in S}$ it is conditionally independent of the other variables, $\{X_n\}_{n \not \in S}$. Our goal is to identify the set $S$ from samples of the variables $X$ and the associated outcomes $Y$. We characterize this problem as a version of the noisy channel coding problem. Using asymptotic information theoretic analyses, we establish mutual information formulas that provide sufficient and necessary conditions on the number of samples required to successfully recover the salient variables. These mutual information expressions unify conditions for both linear and nonlinear observations. We then compute sample complexity bounds for the aforementioned models, based on the mutual information expressions in order to demonstrate the applicability and flexibility of our results in general sparse signal processing models.

The world's most prestigious startup school launched 48 companies today at part 2 of its Summer 2016 Demo Day. Nanoparticle analytics and delivery robots were amongst the products revealed in the B2B, biotech, enterprise, edtech, fintech, and hardware verticals. You can check out our write-ups of all 44 startups that launched yesterday, and TechCrunch's picks for the top 7 from the batch. Trying to distill trends from the hodgepodge of startups at Demo day can be futile, because the real winners are the ones ahead of the trends. For example, TechCrunch thought Airware's drone operating system was a little too early in 2013. It turned out to be smartly ahead of the curve. Now you see lots of drone startups in YC, but many are chasing Airware which has gone on to raise 70 million. Y Combinator president Sam Altman explains "The best company at any given Demo Day is not the one that fits the theme of that Demo Day. Altman cites the Alan Kay quote that "the best way to predict the future is to invent it", adding "I think short of that, the future is basically unknowable. What I like about YC is the companies get to invent the future. They don't have to guess." One important development is that 30% of this batch's companies were founded outside the US, a bigger portion than in the past. YC partner Justin Kan credits that to the program being around long enough that it's funded successful companies from tons of countries.

American space agency NASA has built an Artificial Intelligence (AI) platform capable of aiding firefighters when they enter a burning building. The platform, called Audrey, is the product of a partnership between the agency's Jet Propulsion Lab (JPL) and the Department for Homeland Security (DHS). This project forms part of the Next Generation First Responder program, which aims to identify ways firefighters, police and paramedics can stay safe while in the field. Audrey collects data about heat, gases and other signs of danger to help first responders get through the flames safely and quickly, letting them save victims. To make the AI platform possible, the designers used several technologies developed by NASA and the Department of Defence.

A new remotely controlled war machine for the Iraqi Security Forces. Mosul wasn't supposed to fall again. The city in Northern Iraq saw intense fighting between American forces and Saddam Hussein's sons in 2003, and in 2008 saw major campaign by American troops against insurgent forces. When ISIS attacked Mosul in summer 2014, two divisions of the Iraqi Security Forces were supposed to hold the city, but the 30,000 defenders crumbled and fled from the onslaught of under 2,000 enemies. Mosul remains in ISIS' hands today.

Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the incremental approaches are used for single view data. As large multi-view data generated from multiple sources becomes prevalent nowadays, there is a need for incremental clustering approaches to handle both large and multi-view data. In this paper we propose a new incremental clustering approach called incremental minimax optimization based fuzzy clustering (IminimaxFCM) to handle large multi-view data. In IminimaxFCM, representatives with multiple views are identified to represent each cluster by integrating multiple complementary views using minimax optimization. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IminimaxFCM are provided. Experimental studies on several real world multi-view data sets have been conducted. We observed that IminimaxFCM outperforms related incremental fuzzy clustering in terms of clustering accuracy, demonstrating the great potential of IminimaxFCM for large multi-view data analysis.

Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.

It appears Samsung is working on a competitor to Amazon Alexa and Google Home, with its own take on a voice assistant speaker, according to a filing with the US Federal Communications Commission spotted by Ausdroid. However, the FCC leak has images of a small, white speaker allegedly called the "Samsung Scoop". Documentation points to an internal microphone and Bluetooth within the speaker -- likely to take voice commands like the Amazon Alexa and connect to smartphones. Functionality isn't discussed in the filing, so it's not clear what type of commands or information the Scoop is able to serve up. Exact dimensions of the Scoop aren't known.

Scientists have released incredible pictures of sunken light aircraft carrier USS Independence that were taken by underwater robots exploring the wreck. The historic ship, which served in World War II and was used in the atomic tests at Bikini Atoll in the Pacific, was intentionally sunk off California in 1951. The 622-foot-long Independence sits in 2,600 feet of water in the Greater Farallones National Maritime Sanctuary. Experts on the research vessel E/V Nautilus are using two Remotely Operated Vehicles (ROVs) to study the ship, which has been described as "amazingly intact" by National Oceanic and Atmospheric Administration (NOAA) scientists. The robots' initial dive began on Monday and they have already sent back a number of eerie images from the wreck.

Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels. Do you remember lugging around those old suitcases? If not, good for you - this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his idea to add wheels, because when he pitched his idea people thought he was crazy. Surely no one would want to pull their own suitcase?

The Iraqi army is testing its latest weapon in the fight against ISIS, a remote-controlled battle robot. The unmanned vehicle has a heavy machine gun turret for taking out targets picked out by the controller. Called Alrobot, the bullet-proof vehicle is being tested in the Iraqi desert as part of the army's efforts to retake the city of Mosul from ISIS. Iraq is preparing a remote controlled attack vehicle being which experts say could be used in the fight to retake the city of Mosul from Isis. Reports indicate the remote controlled vehicle, called'Alrobot', was designed by two brothers in Baghdad.