This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF). VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix times a structured coefficient matrix via finding the minimum-volume simplex that encloses all the columns of the data matrix. Recent work showed that VolMin guarantees the identifiability of the factor matrices under mild conditions that are realistic in a wide variety of applications. This paper focuses on both theoretical and practical aspects of VolMin. On the theory side, exact equivalence of two independently developed sufficient conditions for VolMin identifiability is proven here, thereby providing a more comprehensive understanding of this aspect of VolMin. On the algorithm side, computational complexity and sensitivity to outliers are two key challenges associated with real-world applications of VolMin. These are addressed here via a new VolMin algorithm that handles volume regularization in a computationally simple way, and automatically detects and {iteratively downweights} outliers, simultaneously. Simulations and real-data experiments using a remotely sensed hyperspectral image and the Reuters document corpus are employed to showcase the effectiveness of the proposed algorithm.

Listen to Science Friday live on Fridays from 2-4 p.m. ET The most famous patient in neuroscience is the subject of a new book by the grandson of the man who changed his brain forever. Plus, a tour of the particles that could lie outside the Standard Model, and a look at automation in the workforce. City officials plan to repurpose Olympic structures as schools, dormitories, and community parks. What could sterile neutrinos, gravitons, and axions tell us about the Standard Model? A group proposes 20 science-based policy questions for the presidential candidates to address in the months ahead.

Scientists at IBM have claimed a computational breakthrough after imitating large populations of neurons for the first time. Neurons are electrically excitable cells that process and transmit information in our brains through electrical and chemical signals. These signals are passed over synapses, specialised connections with other cells. It's this set-up that inspired scientists at IBM to try and mirror the way the biological brain functions using phase-change materials for memory applications. Using computers to try to mimic the human brain is something that's been theorised for decades due to the challenges of recreating the density and power.

Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. The field of educational technology is mourning a visionary whose work was considered 50 years ahead of its time. Seymour Papert, who died July 31 at age 88, was a mathematician and computer scientist who spent decades at MIT. "Seymour was one of the very first people to recognize that new computer technologies could be used by kids to create things in new ways and express themselves," Mitchel Resnick, a professor of learning research at MIT and a longtime colleague and friend, told NPR Ed. "It's amazing that Seymour was thinking these ideas in the 1960s," Resnick adds, "when computers cost hundreds of thousands of dollars, but he foresaw the day that every child would have access to a computer." The great theme of Papert's work and life was the nature of intelligence, or what he called thinking about thinking.

Machine learning is a fascinating study, but how do you actually use it on your own problems? You may be confused as to how best prepare your data for machine learning, which algorithms to use or how to choose one model over another. In this post you will discover a 14-part crash course into applied machine learning using the Weka platform without a single mathematical equation or line of programming code. Applied Machine Learning With Weka Mini-Course Photo by Leon Yaakov, some rights reserved. Before we get started, let's make sure you are in the right place.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

Today, deep learning is just a cool technology, but it could mean that you won't qualify for tomorrow's best-paying jobs, even if you work in IT. Artificial intelligence (AI) is already shaking things up at Google, Facebook and IBM, and it is going to affect how work is performed in all sectors. There's already been plenty of discussion about AI supplanting so-called blue-collar work, such as trucking and railroad track inspection, but it won't stop at those seemingly easier targets. For a number of reasons, white-collar jobs could be an even more inviting target for machine learning and related technologies. Many experts have highlighted the professional impact of AI and other automation technologies, but none more presciently than Richard and Daniel Susskind in their recent book "The Future of the Professions."

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

The first UK Robotics Week took place from 27 June to 1 July, with events and activities around the country showcasing leading UK technology and engineering research in robotics and autonomous design. Co-ordinated by the Engineering and Physical Sciences Research Council's UK Robotics and Autonomous Systems (UK-RAS) Network, and supported by the Royal Academy of Engineering, the Institution of Engineering and Technology and the Institution of Mechanical Engineers, the week sees robotic research groups from around the world visiting the UK to demonstrate their latest technologies in surgical robotics, field robotics, autonomous driving and unmanned aerial vehicles, while other challenges are set to engage and inspire school, college and university students. On 29 June, the Academy hosted a panel discussion on robotic ethics. The event also featured a Pepper and a NAO robot, from London Design and Engineering UTC, which will be some of the first in the UK to be used in an educational setting. The panel discussion can be found on Twitter at @RAEngNews.

Vernor Vinge once stated in his book The Singularity, "We are on the edge of change comparable to the rise of human life on Earth." As AI now undoubtedly plays a pivotal role in many industries, its risks and repercussions simply cannot be ignored; and shining a light upon these has never been more imperative. That's why, in response to the White House's Notice Of Request For Information (RFI), IBM has created what seems to be an AI 101--covering the huge field of AI and its vast potential applications. "The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields," the RFI summary read. IBM's AI 101 consisted of a numbered list of topics in a somewhat re-ordered and slightly re-factored response to the RFI's questions.