In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light-weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed $D^3$ model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.

It's an event that plays out thousands of times across the UK every day. A consumer tries to pay for their weekly grocery shop using a credit card but with the bags packed at the till is unexpectedly told that it has been'declined'. The card is well within its credit limit, the PIN number is correct, the consumer has made numerous other purchases in the preceding weeks and yet there is no way around the reality of having no plastic money to spend. For the financial services industry these'false positives' have become a growing issue. As well as annoying customers and merchants they cost the industry in terms of the manual intervention necessary to authenticate customers and unblock cards.

Every spring, inventor Dean Kamen hosts his own sort of March Madness: a spectacle in which high-school students compete in events around the world surrounded by cheerleaders, music and entertainment. But rather than playing basketball, they're focused on building robots, an effort that culminates at the championship in St. Louis at the end of this month. Mr. Kamen, 65, is known for coming up with the Segway (the two-wheeled electric vehicle), the iBot (a stair-climbing wheelchair) and a portable dialysis machine. He considers the First Robotics Competition, now in its 25th season, one of his best ideas yet. While many young adults look up to athletes and actors as their heroes, Mr. Kamen hopes his competition--designed like a sports event, with regional brackets--will show them that there are other kinds of stars. "If you think that you can be a superstar in sports or entertainment and make the really big bucks, imagine being a superstar in tech," he says.

We provide a dependency parser for English tweets, TweeboParser . The parser is trained on a subset of a new labeled corpus for 929 tweets (12,318 tokens) drawn from the POS-tagged tweet corpus of Owoputi et al. (2013), Tweebank . These were created by Lingpeng Kong, Nathan Schneider, Swabha Swayamdipta, Archna Bhatia, Chris Dyer, and Noah A. Smith. Given a tweet, TweeboParser predicts its syntactic structure, represented by unlabeled dependencies. Since a tweet often contains more than one utterance, the output of TweeboParser will often be a multi-rooted graph over the tweet.

It is built upon one widely used machine learning package scikit-learn and two scientific computing packages Numpy and Scipy. It serves as a platform for facilitating feature selection application, research and comparative study. It is designed to share widely used feature selection algorithms developed in the feature selection research, and offer convenience for researchers and practitioners to perform empirical evaluation in developing new feature selection algorithms. Instructions of using this repository can be found in our project webpage at http://featureselection.asu.edu/

A year ago, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways.

A recent article titled "Why is AI Female?" made the connection that gendered labor, in service professions in particular, is fueling our expectations for gendered AI assistants and service robots. Furthermore, the author argues, this "feminizing -- and sexualizing -- of machines" signals a future with a disproportionate use of feminized VR and robots for a male-dominated sex industry. "Sex with robots is a big leap from asking Siri to set an alarm, but the fact that we've largely equated artificial intelligence with female personalities is worth examining. There are, after all, few sexualized male robots or avatars." Herbert Televox and Mr. Telelux, the early 20th century robots made by Westinghouse, were both male.

Neurensic, an artificial intelligence (AI) technology startup focused on software-as-a-service solutions for the financial services industry, has announced the formation of a new Innovation Lab, which will focus on applying its big data aggregation and AI capabilities to nascent technologies such as blockchain. "Neurensic's ultimate goal is to bring together all business processes currently completed post-trade into a single vendor platform that is able to function real-time and at-trade, opening new horizons for self-regulating markets and ultimately redefining all financial transactions. I have faith that our new Innovation Lab will accelerate this process", said said David Widerhorn, Chief Executive Officer. This lab will be led by Neurensic co-founder Zachary Watts, who is being named as Chief Innovation Officer. "Bringing a business intelligence layer to distributed ledger solutions is critical to forming a sustainable path toward adopting these technologies in the long-run," said Watts.

Google's DeepMind artificial intelligence team is making history. Its AlphaGo program is up 2-0 on Lee Sedol, one of the top Go players alive. This is the first time a computer has beat a human champion without a handicap. While there will be three more games between Sedol and AlphaGo, the victory suggests that Go is the latest game that computers are outwitting people in. Checkers fell in 1994, Chess in 1997, and Jeopardy in 2011. "It there's a social component with players playing together, it's not clear," Littman says.

Until recently, Robyn Ewing was a writer in Hollywood, developing TV scripts and pitching pilots to film studios. Now she's applying her creative talents towards building the personality of a different type of character – a virtual assistant, animated by artifical intelligence (AI), that interacts with sick patients. Ewing works with engineers on the software program, called Sophie, which can be downloaded to a smartphone. The virtual nurse gently reminds users to check their medication, asks them how they are feeling or if they are in pain, and sends data to a real doctor. As tech behemoths and a wave of start-ups double down on virtual assistants that can chat with human beings, writing for AI is becoming a hot job in Silicon Valley.