If you want to know whether you're at risk of Alzheimer's disease, taking this test could help you. Those unable to tell which character is the odd one out could well be at risk of the devastating illness. Known as Greebles, the little purple characters have been designed by scientists in their ongoing quest for a cure. A study found those at genetic risk of the disease struggle to distinguish a subtle difference in one of the images. But they may be unaware of their likelihood as they are still able to detect minor changes in people's faces, objects and scenes.

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.

High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware. This malware was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.

We study streaming principal component analysis (PCA), that is to find, in $O(dk)$ space, the top $k$ eigenvectors of a $d\times d$ hidden matrix $\bf \Sigma$ with online vectors drawn from covariance matrix $\bf \Sigma$. We provide $\textit{global}$ convergence for Oja's algorithm which is popularly used in practice but lacks theoretical understanding for $k>1$. We also provide a modified variant $\mathsf{Oja}^{++}$ that runs $\textit{even faster}$ than Oja's. Our results match the information theoretic lower bound in terms of dependency on error, on eigengap, on rank $k$, and on dimension $d$, up to poly-log factors. In addition, our convergence rate can be made gap-free, that is proportional to the approximation error and independent of the eigengap. In contrast, for general rank $k$, before our work (1) it was open to design any algorithm with efficient global convergence rate; and (2) it was open to design any algorithm with (even local) gap-free convergence rate in $O(dk)$ space.

Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative regularization path with structural sparsity where the parameter is sparse under some linear transforms, based on variable splitting and the Linearized Bregman Iteration. Hence it is called \emph{Split LBI}. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is presented that equipped with a proper early stopping, Split LBI may achieve model selection consistency under a family of Irrepresentable Conditions which can be weaker than the necessary and sufficient condition for generalized Lasso. Furthermore, some $\ell_2$ error bounds are also given at the minimax optimal rates. The utility and benefit of the algorithm are illustrated by several applications including image denoising, partial order ranking of sport teams, and world university grouping with crowdsourced ranking data.

There has been great interest recently in applying nonparametric kernel mixtures in a hierarchical manner to model multiple related data samples jointly. In such settings several data features are commonly present: (i) the related samples often share some, if not all, of the mixture components but with differing weights, (ii) only some, not all, of the mixture components vary across the samples, and (iii) often the shared mixture components across samples are not aligned perfectly in terms of their location and spread, but rather display small misalignments either due to systematic cross-sample difference or more often due to uncontrolled, extraneous causes. Properly incorporating these features in mixture modeling will enhance the efficiency of inference, whereas ignoring them not only reduces efficiency but can jeopardize the validity of the inference due to issues such as confounding. We introduce two techniques for incorporating these features in modeling related data samples using kernel mixtures. The first technique, called $\psi$-stick breaking, is a joint generative process for the mixing weights through the breaking of both a stick shared by all the samples for the components that do not vary in size across samples and an idiosyncratic stick for each sample for those components that do vary in size. The second technique is to imbue random perturbation into the kernels, thereby accounting for cross-sample misalignment. These techniques can be used either separately or together in both parametric and nonparametric kernel mixtures. We derive efficient Bayesian inference recipes based on MCMC sampling for models featuring these techniques, and illustrate their work through both simulated data and a real flow cytometry data set in prediction/estimation, cross-sample calibration, and testing multi-sample differences.

Robert W. Taylor, who was instrumental in creating the internet and the modern personal computer, has died. Taylor, who had Parkinson's disease, died Thursday at his home in the San Francisco Peninsula community of Woodside, his son, Kurt Taylor, told the Los Angeles Times and the New York Times. In 1961, Taylor was a project manager for NASA when he directed funding to Douglas Engelbart at the Stanford Research Institute, who helped develop the modern computer mouse. Taylor was working for the Pentagon's Advanced Research Projects Agency in 1966 when he shepherded the creation of a single computer network to link ARPA-sponsored researchers at companies and institutions around the country. Taylor was frustrated that he had to use three separate terminals to communicate with the researchers through their computer systems.

Whether we like to believe it or not, scientific research has clearly shown that we all have deeply ingrained biases, which create stereotypes in our mind that can often lead to unfair treatment of others. As artificial intelligence (AI) plays an increasingly important role in our lives as decision makers in self-driving cars, doctor offices, and surveillance, it becomes critical to ask whether AI exhibits the same inbuilt biases as humans. According to a new study conducted by a team of researchers at Princeton, many AI systems do in fact exhibit racial and gender biases that could prove problematic in some cases. One well established way for psychologists to detect biases is the Implicit Association Test. Introduced into the scientific literature in 1998 and widely used today in clinical, cognitive, and developmental research, the test is designed to measure the strength of a person's automatic association between concepts or objects in memory.

Robert Taylor, who was instrumental in creating the internet and the modern personal computer, has died. Taylor, who had Parkinson's disease, died on Thursday at his home in the San Francisco peninsula community of Woodside, his son, Kurt Taylor, told the Los Angeles Times and the New York Times. "Any way you look at it, from kick-starting the internet to launching the personal computer revolution, Bob Taylor was a key architect of our modern world," Leslie Berlin, a historian at the Stanford University Silicon Valley Archives project, told the New York Times. In 1961, Taylor was a project manager for Nasa when he directed funding to Douglas Engelbart at the Stanford Research Institute, who helped develop the modern computer mouse. Taylor was working for the Pentagon's Advanced Research Projects Agency (Arpa) in 1966 when he shepherded the creation of a single computer network to link Arpa-sponsored researchers at companies and institutions around the country.

The meaning of "work" is changing, and with life expectancies growing, the gig economy taking hold and artificial intelligence taking plenty of people's jobs, millennials will have careers that are worlds away from those of their forebears, says Dr. Linda Sharkey, global managing director of the consulting firm Achieveblue Inc. Sharkey is the author of "The Future-Proof Workplace: Six Strategies to Accelerate Talent Development, Reshape Your Culture and Succeed with Purpose," co-written with Morag Barnett, the chief executive of the business management consultancy SkyeTeam. She talked to International Business Times about the prospects for 21st-century careers, the falling value of a four-year degree and the idea that a robot might be conducting this sort of question-and-answer article in the not-so-far-away future. This interview has been edited and condensed for clarity. One issue you touch on in your book is your expectation that retirement will cease to be a 21st century phenomenon, and that today's young workers are more likely to take sabbaticals than end their careers by their late sixties. Is this something you think will be born of choice -- a desire to work longer -- or a consequence of the unsustainability of Social Security as generations live longer and have fewer children?