We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings. Unlike existing coordinate descent type algorithms, the SNCD updates each regression coefficient and its corresponding subgradient simultaneously in each iteration. It combines the strengths of the coordinate descent and the semismooth Newton algorithm, and effectively solves the computational challenges posed by dimensionality and nonsmoothness. We establish the convergence properties of the algorithm. In addition, we present an adaptive version of the "strong rule" for screening predictors to gain extra efficiency. Through numerical experiments, we demonstrate that the proposed algorithm is very efficient and scalable to ultra-high dimensions. We illustrate the application via a real data example.

The US Air Force is working on a'space fence' to protect spacecraft from orbiting junk. It plans to activate it in just under two years to replace a similar system that was shut down in 2013. While the military has dubbed the system a'fence', it is in fact a radar system that sends signal into space to track objects the size of a tennis ball. The US Air Force is working on a'space fence' to protect spacecraft from orbiting junk. It plans to activate it in just under two years to replace a similar system that was shut down in 2013.

Google is set to allow its Chromebook laptops to run Android apps in a bit to take on Apple and Microsoft. The laptops run a version of Google's Chrome browser instead of an operating system like Windows - but until now have been unable to download apps. According to market research firm IDC, in Q1 of this year Chromebook shipments overtook Macs in the U.S. The laptops run a version of Google's Chrome browser instead of an operating system like Windows - but until now have been unable to download apps. Later this year, they will be able to run all android apps, Google said. The machine uses Google's Chrome OS, which is based on the firm's web browser.

Identification of patients with overt cardiorespiratory insufficiency or at high risk of impending cardiorespiratory insufficiency is often difficult outside the venue of directly observed patients in highly staffed areas of the hospital, such as the operating room, intensive care unit (ICU) or emergency department. And even in these care locations, identification of cardiorespiratory insufficiency early or predicting its development beforehand is often challenging. The clinical literature has historically prized early recognition of cardiorespiratory insufficiency and its prompt correction as being valuable at minimizing patient morbidity and mortality while simultaneously reducing healthcare costs. Recent data support the statement that integrated monitoring systems that create derived fused parameters of stability or instability using machine learning algorithms, accurately identify cardiorespiratory insufficiency and can predict their occurrence. In this overview, we describe integrated monitoring systems based on established machine learning analysis using various established tools, including artificial neural networks, k?nearest neighbor, support vector machine, random forest classifier and others on routinely acquired non?invasive and invasive hemodynamic measures to identify cardiorespiratory insufficiency and display them in real?time with a high degree of precision.

Recently we walked you through on how to train a sentiment analysis classifier for hotel reviews using Scrapy and MonkeyLearn. This tutorial is a perfect example on how we can combine web scraped data and machine learning for discovering valuable insights about a particular industry. With this model we were able to analyze millions of reviews and understand if guests love or hate different hotels. But besides understanding the sentiment of a review, wouldn't be interesting to understand what particular aspects do the guests love or hate about a particular hotel? This post will cover how you can create a machine learning classifier to understand the different aspects of hotel reviews.

CTO Corner is BITS's monthly publication covering emerging trends and technologies in the financial services industry. Artificial Intelligence (AI), defined as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages, has been around for over 60 years.1 In his 1950 paper "Computing Machinery and Intelligence," Alan Turing opens with: "I propose to consider the question'can machines think?'"2 He proposed a test of a machine's ability to exhibit intelligent behavior, equivalent to, or indistinguishable from, that of a human being, which is now known as the Turing Test.3 AI as an academic discipline began at the famous 1955 Dartmouth conference organized by John McCarthy from Stanford University and Marvin Minsky from MIT.4 This CTO Corner explores both the potential for AI to transform the financial services industry and challenges it presents.

In December 2015 Leman Solicitors hosted the Future of Law event for 100 corporate counsel. We spoke about IBM's Watson which is being used to build A.I. applications in several sectors, notably ROSS in law. It's part of a disruption that we say is going to fundamentally change the way legal services are delivered. Another article in today's Business Insider UK reports: "Ask ROSS to look up an obscure court ruling from 13 years ago, and ROSS will not only search for the case in an instant -- without contest or complaint -- but it'll offer opinions in plain language about the old ruling's relevance to the case at hand." The article can be found here.

Google Inc.'s latest technological marvels point to a future where you'll never need to visit websites, write a term paper or stress over what to buy for your mother's birthday. For consumers seeking convenience and speed, it all sounds great. But how will any of it make Google money? The Mountain View, Calif., company's apparent transition from go-to search engine to omnipresent virtual assistant probably will require rethinking its advertising business. By tracking Web browsing, emails, chats and more, Google has become a dominant force in digital ads.

Silicon Valley companies have long battled one another for your money, your attention, and your data. Now they are fighting one another over a new battleground: Your living room. Google on Wednesday unveiled Google Home, a voice-activated, Internet-connected speaker with a built-in virtual assistant powered by Google's smarts. Home is Google's answer to the Echo, a similar device from Amazon that has become a surprise hit with shoppers. For consumers, Google Home and the Amazon Echo can answer questions about the weather, set alarms, and manage appointments, among other tasks.

Robots can paint, but when it comes to writing, they shouldn't quit their day jobs. That's the combined conclusion from results of two contests announced this week. On the upside, artificial intelligence created some pretty impressive works for RobotArt.org's The contest challenged artists and engineers to create a robot that painted like a real artist. Essentially, the aim was to get "as many teams as possible to set up a robot that can do any sort of painting," the contest site explains.