Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries, which in practice is typically formulated as a weighted low-rank approximation problem and solved by non-convex optimization heuristics such as alternating minimization. In this paper, we provide provable recovery guarantee of weighted low-rank via a simple alternating minimization algorithm. In particular, for a natural class of matrices and weights and without any assumption on the noise, we bound the spectral norm of the difference between the recovered matrix and the ground truth, by the spectral norm of the weighted noise plus an additive error that decreases exponentially with the number of rounds of alternating minimization, from either initialization by SVD or, more importantly, random initialization. These provide the first theoretical results for weighted low-rank via alternating minimization with non-binary deterministic weights, significantly generalizing those for matrix completion, the special case with binary weights, since our assumptions are similar or weaker than those made in existing works. Furthermore, this is achieved by a very simple algorithm that improves the vanilla alternating minimization with a simple clipping step. The key technical challenge is that under non-binary deterministic weights, na\"ive alternating steps will destroy the incoherence and spectral properties of the intermediate solutions, which are needed for making progress towards the ground truth. We show that the properties only need to hold in an average sense and can be achieved by the clipping step. We further provide an alternating algorithm that uses a whitening step that keeps the properties via SDP and Rademacher rounding and thus requires weaker assumptions. This technique can potentially be applied in some other applications and is of independent interest.

Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a quadratic program of size quadratically in the number of classes. We develop distributed algorithms for two all-in-one SVM formulations (Lee et al. and Weston and Watkins) that parallelize the computation evenly over the number of classes. This allows us to compare these models to one-vs.-rest SVMs on unprecedented scale. The results indicate superior accuracy on text classification data.

IMAGE: The Argonne research team that has pioneered the use of machine learning tools in 2-D material modeling. Machine learning, a field focused on training computers to recognize patterns in data and make new predictions, is helping doctors more accurately diagnose diseases and stock analysts forecast the rise and fall of financial markets. And now materials scientists have pioneered another important application for machine learning -- helping to accelerate the discovery and development of new materials. Researchers at the Center for Nanoscale Materials and the Advanced Photon Source, both U.S. Department of Energy (DOE) Office of Science User Facilities at DOE's Argonne National Laboratory, announced the use of machine learning tools to accurately predict the physical, chemical and mechanical properties of nanomaterials. In a study published in The Journal of Physical Chemistry Letters, a team of researchers led by Argonne computational scientist Subramanian Sankaranarayanan described their use of machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a two-dimensional (2-D) material made up of a one-atom-thick sheet of tin.

Data Science Middle East Foundation in partnership with EVERATI running 3-day training workshop series across Middle East to get you started on your data science and machine learning journey, as you learn how to use data and science to deliver insights, value and innovation. Data Science and Machine Learning workshop is a 3-day practical training program for applied introduction to data science industry practices and models of machine learning. The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the workshop, participants will be ready to implement the machine learning algorithms using data science on their own data, and immediately generate business value. The workshop will take participants through the conceptual and applied foundations of the subject.

In recent years, advanced machine learning techniques have enabled computers to recognize objects in images, understand commands from spoken sentences, and translate written language. But while consumer products like Apple's Siri and Google Translate might operate in real time, actually building the complex mathematical models these tools rely on can take traditional computers large amounts of time, energy, and processing power. As a result, chipmakers like Intel, graphics powerhouse Nvidia, mobile computing kingpin Qualcomm, and a number of startups are racing to develop specialized hardware to make modern deep learning significantly cheaper and faster. The importance of such chips for developing and training new AI algorithms quickly cannot be understated, according to some AI researchers. "Instead of months, it could be days," Nvidia CEO Jen-Hsun Huang said in a November earnings call, discussing the time required to train a computer to do a new task.

Now that we have the data ready for analysis, we first create a histograms of average restaurant rating group by restaurant grades. This is the first image presented above. For a second visualization, I created another histogram plot of average restaurant ratings grouped by number of comments which included a foodborne illness flag.

A new study has revealed that women who are optimistic are less likely to die from cancer, heart disease, stroke, infection, and several other major causes of death. The positive health effects for the women were shown to last over eight years. The study's most optimistic women had a nearly 30 per cent lower risk of death from major diseases than its least optimistic women. Do plants learn like humans? Smart seedlings can be taught...

IBM has launched the Watson for Cyber Security beta program to encourage companies to include Watson in their current security environments. Starting off with such organizations as California Polytechnic State University, Sumitomo Mitsui Banking Corporation, and University of Rochester Medical Center, the program will grow over the next few weeks to encompass 40 companies spanning industries like banking, travel, energy, automotive, health care, insurance, and education. For the past few months, IBM Security has been working with eight universities -- California State Polytechnic University at Pomona, Penn State, MIT, New York University, University of Maryland at Baltimore County, and Canada's universities of New Brunswick, Ottawa, and Waterloo -- to help teach Watson the "language of cybersecurity." The research project involved feeding Watson's AI brain thousands of documents annotated to help the system understand what a threat is, what it does, and what indicators are related. Watson for Cyber Security combines machine learning and natural language processing to make associations in unstructured data like blogs, research reports, and documentation that security analysts can then use to make better, faster decisions.

Propulsion expert warns NASA's'impossible drive' results could be caused by a'mundane error' in experiments EmDrive creates thrust by bouncing microwaves around a chamber The system has caused a stir as it it'goes against' the laws of physics A paper from NASA engineers shows the technology works in a vacuum Expert says the findings are likely the result of a mundane experimental error The system has caused a stir as it it'goes against' the laws of physics A fuel-free engine, described as'impossible' to create, may now be a step closer to reality, according to leaked Nasa documents. Fifty shades of Pompeii: Erotic wall paintings reveal the... Elon Musk forced to keep SpaceX rockets grounded until... Incredible X-rays show how lithium-ion batteries explode:... Know your place, robots! Fifty shades of Pompeii: Erotic wall paintings reveal the... Elon Musk forced to keep SpaceX rockets grounded until... Incredible X-rays show how lithium-ion batteries explode:... Know your place, robots! The tests managed to generate powers of 1.2 millinewtons per kilowatt (mN/Kw), a fraction of the current state of the art Hall ion thruster, which can achieve a massive 60 mN/Kw (illustrated) In the new study, which tested if the device could operate in a vacuum, the researchers found that'thrust data from forward, reverse, and null suggested that the system was consistently performing at 1.2 0.1 mN/kW1.2 The technology has been dubbed the'warp drive' for its similarity to the fictional propulsion system seen in the Star Trek series Leaked NASA paper shows the'impossible' EM Drive really does work - ScienceAlert EmDrive: Leaked Nasa peer review paper replicates Roger Shawyer's 2006 results Q-Thruster In-Vacuum Fall 2015 Test Report.pdf

The MGH Center for Clinical Data Science at Massachusetts General Hospital was founded to build a smarter healthcare system that will change the way the world practices medicine. Our intimate knowledge of healthcare's greatest challenges, decades of clinical experience and vast stores of biomedical data combined with the latest advances in cognitive computing will lead to new ways of detecting, diagnosing and treating disease. We use artificial intelligence to build and commercialize systems and tools that enhance outcomes, improve efficiency and focus on patients. What is the MGH Center for Clinical Data Science? A fast-growing startup within one of the world's oldest academic medical centers A data-obsessed team of machine learning gurus, software engineers, doctors and scientists A place where innovative products are born, tested and put into clinical practice A community of researchers and industry partners with a passion to improve human health