Structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagate. The appeal of SEMs in these settings stems from their simplicity and tractability, since they typically assume linear dependencies among observable variables. Acknowledging the limitations inherent to adopting linear models, the present paper advocates nonlinear SEMs, which account for (possible) nonlinear dependencies among network nodes. The advocated approach leverages kernels as a powerful encompassing framework for nonlinear modeling, and an efficient estimator with affordable tradeoffs is put forth. Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, and is amenable to proximal-splitting optimization methods. To this end, solvers with complementary merits are developed by leveraging the alternating direction method of multipliers, and proximal gradient iterations. Experiments conducted on simulated data demonstrate that the novel approach outperforms linear SEMs with respect to edge detection errors. Furthermore, tests on a real gene expression dataset unveil interesting new edges that were not revealed by linear SEMs, which could shed more light on regulatory behavior of human genes.

Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.

Here are this week's top stories in fraud and big data technology: Thanks to its huge network of users, Sprint has access to vast amounts of user data. Three years ago it established subsidiary Pinsight Media to investigate ways of capitalizing on that data. Since then it has gone from serving zero to six billion ad impressions per months, based on "authenticated first party data" which it alone has access to. The fact that plain passwords are no longer safe to protect our digital identities is no secret. For years, the use of two-factor authentication (2FA) and multi-factor authentication (MFA) as a means to ensure online account security and prevent fraud has been a hot topic of discussion.

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

The IBM Watson IoT Platform allows people to securely and easily connect devices, from chips and intelligent appliances, to applications and industry solutions, and use cognitive services including natural language processing and machine learning for deep insights into hidden or "dark" data for innovation and transformation. The platform also provides the ability to analyze sensor data from billions of devices, to better forecast weather events, for example. During the hackathon, developers will use IBM Watson IoT cognitive APIs and smart devices to create conversational interfaces like chatbots. "People are excited about chatbots because they see the potential to interact with technology using conversational language," said Damian McCabe, VP Engineering at Connected Lab. "As an IBM alumni, I'm excited about the potential of the IBM Watson's cognitive capabilities including natural language processing and machine learning. At the hackathon, developers will use this powerful platform to build more human ways for people to use innovative technology."

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

Lynda Carter, best known for her portrayal of the original Wonder Woman, will be presented with a lifetime achievement award at the 41st annual Gracie Awards Gala, Variety has learned. The Gracie Awards recognize programming created by, for and about women in all media. Carter starred as the titular female superhero in "Wonder Woman," an adventure-drama series that ran on ABC and later CBS from 1975-1979. In the 2000s she guest starred on TV shows including "Law & Order," "Law & Order: SVU," "Smallville" and "Two and a Half Men." The actress also appeared in the 2005 movie reboot of "Dukes of Hazzard" alongside Johnny Knoxville and Jessica Simpson, and she later lent her voice to video games such as "The Elder Scrolls" series and "Fallout 4." Aside from acting, Carter devoted herself to singing, beauty pageants and charity work.

LOS ALAMOS, N.M., May 9, 2016--Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

The Smart Data Online Conference will bring together the insights and training that are helpful in navigating new technologies shared across data management topics such as machine learning, cognitive computing, and artificial intelligence to name a few. It is in this event that we can connect you, the attendee, with the speakers who are working successfully with these new technologies in their daily positions.

This post is a discussion of Nick Bostrom's book "Superintelligence". The book has had an effect on the thinking of many of the world's thought leaders. In that light, and given this series of blog posts is about the "Future of AI", it seemed important to read the book and discuss his ideas. In an ideal world, this post would certainly have contained more summaries of the books arguments and perhaps a later update will improve on that aspect. For the moment the review focuses on counter-arguments and perceived omissions (the post already got too long with just covering those). Bostrom considers various routes we have to forming intelligent machines and what the possible outcomes might be from developing such technologies. He is a professor of philosophy but has an impressive array of background degrees in areas such as mathematics, logic, philosophy and computational neuroscience. So let's start at the beginning and put the book in context by trying to understand what is meant by the term "superintelligence" In common with many contributions to the debate on artificial intelligence, Bostrom never defines what he means by intelligence. Obviously, this can be problematic. On the other hand, superintelligence is defined as outperforming humans in every intelligent capability that they express. Personally, I've developed the following definition of intelligence: "Use of information to take decisions which save energy". Here by information I might mean data or facts or rules, and by saving energy I mean saving'free' energy.1 However, accepting Bostrom's lack of definition of intelligence (and perhaps taking note of my own), we can still consider the routes to superintelligence Bostrom proposes.