The researchers use a text-mining algorithm to prioritize research papers to read and include in their Comparative Toxicogenomics Database (CTD), a public database that manually curates and codes data from the scientific literature describing how environmental chemicals interact with genes to affect human health.

To help select the most relevant papers for inclusion in the CTD, Thomas Wiegers, a research bioinformatician at NC State and the other co-lead author of the report, developed a sophisticated algorithm as part of a text-mining process. The application evaluates the text from thousands of papers and assigns a relevancy score to each document.

But how good is the algorithm at determining the best papers? To test that, the researchers text-mined 15 000 articles and sent a representative sample to their team of biocurators to manually read and evaluate on their own, blind to the computer’s score. The biocurators concurred with the algorithm 85 percent of the time with respect to the highest-scored papers.

Using the algorithm to rank papers allowed biocurators to focus on the most relevant papers, increasing productivity by 27 percent and novel data content by 100 percent.

There are always outliers in these types of experiments: occasions where the algorithm assigns a very high score to an article that a human biocurator quickly dismisses as irrelevant. The team that looked at those outliers was often able to see a pattern as to why the algorithm mistakenly identified a paper as important.

(The paper, “Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the Comparative Toxicogenomics Database,” was published online April 17 in PLOS ONE. Co-authors are Dr. Cindy Murphy, a biocurator scientist at NC State; Dr. Carolyn Mattingly, associate professor of biology at NC State; and Drs. Robin Johnson, Jean Lay, Kelley Lennon-Hopkins, Cindy Saraceni-Richards and Daniela Sciaky from The Mount Desert Island Biological Laboratory.)

Researchers believe that in the future a 3D microchip would enable additional storage capacity on chips by allowing information to be spread across several layers instead of being compacted into one layer, as is currently the case.

For the research, the Cambridge scientists used a special type of microchip called a spintronic chip which exploits the electron’s tiny magnetic moment or ‘spin’ (unlike the majority of today’s chips which use charge-based electronic technology). Spintronic chips are increasingly being used in computers, and it is widely believed that within the next few years they will become the standard memory chip.

To create the microchip, the researchers used an experimental technique called ‘sputtering’. They effectively made a club-sandwich on a silicon chip of cobalt, platinum and ruthenium atoms. The cobalt and platinum atoms store the digital information in a similar way to how a hard disk drive stores data. The ruthenium atoms act as messengers, communicating that information between neighbouring layers of cobalt and platinum. Each of the layers is only a few atoms thick.

They then used a laser technique called MOKE to probe the data content of the different layers. As they switched a magnetic field on and off they saw in the MOKE signal the data climbing layer by layer from the bottom of the chip to the top. They then confirmed the results using a different measurement method.

As the Victoria & Alfred Waterfront in Cape Town is the most popular tourist spot in South Africa, the site launched with the all heritage sites and famous landmarks in the area.

Work on the next phase has already started, all the heritage sites on Robben Island, a World Heritage Site, where Nelson Mandela was a prisoner for many years.

The third phase will start with the most famous of the city’s many natural wonders, Table Mountain. The city centre at the foot of the mountain will be mapped, followed by the suburbs of the Atlantic seaboard in the following phases.
Cape-Town-Heritage.co.za aims to map all the heritage sites and famous landmarks in the Cape Peninsula.

The History of Home Heating [Infographic] – An infographic by the team at Global Home Improvements

With the development of the RoboEarth Cloud Engine the team continues their work towards creating an Internet for robots. The new platform extends earlier work on allowing robots to share knowledge with other robots via a WWW-style database, greatly speeding up robot learning and adaptation in complex tasks.

The developed Platform as a Service (PaaS) for robots allows to perform complex functions like mapping, navigation, or processing of human voice commands in the cloud, at a fraction of the time required by robots’ on-board computers. By making enterprise-scale computing infrastructure available to any robot with a wireless connection, the researchers believe that the new computing platform will help pave the way towards lighter, cheaper, more intelligent robots.

“The RoboEarth Cloud Engine is particularly useful for mobile robots, such as drones or autonomous cars, which require lots of computation for navigation. It also offers significant benefits for robot co-workers, such as factory robots working alongside humans, which require large knowledge databases, and for the deployment of robot teams.” says Mohanarajah Gajamohan, researcher at the Swiss Federal Institute of Technology (ETH Zurich) and Technical Lead of the project.

“On-board computation reduces mobility and increases cost.”, says Dr. Heico Sandee, RoboEarth’s Program Manager at Eindhoven University of Technology in the Netherlands, “With the rapid increase in wireless data rates caused by the booming demand of mobile communications devices, more and more of a robot’s computational tasks can be moved into the cloud.”

While high-tech companies that heavily rely on data centers have been criticized for creating fewer jobs than traditional companies (e.g., Google or Facebook employ less than half the number of workers of General Electric or Hewlett-Packard per dollar in revenue), the researchers don’t believe that this new robotics platform should be cause for alarm. According to a recent study by the International Federation of Robotics and Metra Martech entitled “Positive Impact of Industrial Robots on Employment”, robots don’t kill jobs but rather tend to lead to an overall growth in jobs.

Research to date on human thinking suggests that our judgment is often biased because we are intellectually lazy, or so-called cognitive misers. We intuitively substitute hard questions for easier ones. What is less clear is whether or not we realize that we are doing this and notice our mistake.

Using an adaptation of the standard ‘bat-and-ball’ problem, the researchers explored this phenomenon. The typical ‘bat-and-ball’ problem is as follows: a bat and ball together cost $1.10. The bat costs $1 more than the ball. How much does the ball cost? The intuitive answer that immediately springs to mind is 10 cents. However, the correct response is 5 cents.

The authors developed a control version of this problem, without the relative statement that triggers the substitution of a hard question for an easier one: A magazine and a banana together cost $2.90. The magazine costs $2. How much does the banana cost?

A total of 248 French university students were asked to solve each version of the problem. Once they had written down their answers, they were asked to indicate how confident they were that their answer was correct.

Only 21 percent of the participants managed to solve the standard problem (bat/ball) correctly. In contrast, the control version (magazine/banana) was solved correctly by 98 percent of the participants. In addition, those who gave the wrong answer to the standard problem were much less confident of their answer to the standard problem than they were of their answer to the control version. In other words, they were not completely oblivious to the questionable nature of their wrong answer. The key reason seems to be that reasoners tend to minimize cognitive effort and stick to intuitive processing.

The authors comment: “Although we might be cognitive misers, we are not happy fools who blindly answer erroneous questions without realizing it.”

Indeed, although people appear to unconsciously substitute hard questions for easier ones, in reality, they are less foolish than psychologists might believe because they do know they are doing it.

Now, a Michigan State University business scholar and colleagues have developed the most accurate model yet for targeting those fast-moving minnows. The research-based model predicts when during the day people use the varying forms of media and even when they are using two or more at a time, an increasingly common practice known as media multiplexing.

That’s good news for companies struggling to predict when to buy ads on the Internet, television and radio, and in print publications. Previous models for predicting when consumers use media were 60 percent-70 percent accurate; the new model led by MSU’s Chen Lin has proved 97 percent accurate.

The study, published in the academic journal Marketing Science, is based on a survey of the media-consumption habits of nearly 2 000 U.S. residents. Lin and colleagues used the survey data to create their complex forecasting model.

Among the study findings:

• People spend about 35 percent of their time consuming media.
• Television is still the most popular outlet, followed by computer.
• During the weekend, consumers spend more time watching TV and reading print publications and less time on the computer and listening to radio.
• People spend about 1.5 hours a day consuming multiple media at the same time (e.g., surfing the Web while watching TV). This happens more at during the start of the workday and before bed – at about 9 a.m. and again at 9 p.m.
• Chen also said she was surprised to find consumers still value print media at certain times, particularly in the morning. Print was especially popular when it was paired with other forms of media.

Lin said ad buyers should stop considering the different media forms as competing and instead view them as complimentary. For example, print ads should be partnered with radio and Internet media forms in the key time slots when consumers are likely to be using all three forms.

Face-to-face interactions with front desk clerks and concierges are not essential for personalized service, and increasingly these encounters are being substituted with Smartphone apps and other automated service systems, according to a study in the current edition of the Journal of Service Research.

Business travelers who frequent the same hotels time and again may develop personal relationships with certain concierges over time, but a “smart digital assistant” app can provide consistent personalized recommendations for every customer, every time, no matter where they are, the researchers report.

Service providers can collect information about customer preferences through methods such as customer satisfaction forms and tracking “likes” on Facebook or other social media. But automated service systems and applications can capture both explicit and implicit feedback, and do it in the most complete and effective way possible. Already, service systems can record customers’ choices, track navigation or record web browsing behavior. Almost instantaneously, these systems can exploit that information to personalize future recommendations.

Co-authors Professor Robert J. Glushko and Karen Joy Nomorosa envision an app that personalizes customer experiences by developing personalized systems that integrate across all service platforms, from hotels, to dining, to booking flights.

Of course, there are still customers who enjoy the “lazy chatty conversation with a bank teller or hotel front desk clerk,” Nomorosa points out, but “for every customer who enjoys a lazy chat, there is surely someone who wants a minimalist information-driven experience.”