A mathematical model that has been used for more than 80 years to determine the hunting range of animals in the wild holds promise for mapping the territories of street gangs, a UCLA-led team of social scientists reports in a new study.
An interview with Marcus Buckingham, founder of TMBC and author of StandOut. For more, visit the HBR article Leadership Development in the Age of the Algorithm.
New algorithm could substantially speed up MRI scans | Medical Xpress
Magnetic resonance imaging (MRI) devices can scan the inside of the body in intricate detail, allowing clinicians to spot even the earliest signs of cancer or other abnormalities. But they can be a long and uncomfortable experience for patients, requiring them to lie still in the machine for up to 45 minutes.
Now this scan time could be cut to just 15 minutes, thanks to an algorithm developed at MIT’s Research Laboratory of Electronics.
MRI scanners use strong magnetic fields and radio waves to produce images of the body. Rather than taking just one scan of a patient, the machines typically acquire a variety of images of the same body part, each designed to create a contrast between different types of tissue. By comparing multiple images of the same region, and studying how the contrasts vary across the different tissue types, radiologists can detect subtle abnormalities such as a developing tumor. But taking multiple scans of the same region in this way is time-consuming, meaning patients must spend long periods inside the machine
a collection of movie clips in which Mathematics appears.
Scientists Turn Brain’s Visual Memories into a Mind-Blowing Video
To be able to do this, the researches used functional Magnetic Resonance Imaging (fMRI) to measure the blood flow through brain’s visual cortex. Then, different parts of the brain were divided into volumetric pixels or voxels (the term might be familiar to those who remember early 3D games which were based on voxels instead of polygons which are more commonly used today). Finally, the scientists built a computational model which describes how visual information is mapped into brain activity.
How an Algorithm Helped Arrange the Names on the 9/11 Memorial:
The memorial’s arrangement preserves, for instance, the terrible blow suffered by the investment bank Cantor Fitzgerald. Before the morning of September 11, the firm’s headquarters occupied several floors high in the North Tower of the World Trade Center (WTC). In the first of the terrorist attacks of that day, a hijacked airliner struck the North Tower, just below Cantor’s offices. The firm was devastated; 658 employees died in the attack, along with 46 contractors, food-service workers, consultants and visitors.
Although no heading identifies them as such, the 704 names of those killed at Cantor Fitzgerald appear together on the memorial. Cantor’s loss was so great that its portion of the memorial surrounds almost half of the north pool. Within that grouping, as elsewhere on the memorial, the placement of names also reflects numerous other social and professional connections, thanks to input from families and co-workers and some heavy lifting by a custom-built computer algorithm.
(via Scientific American)
via jtotheizzoe:
(via jtotheizzoe)
“Researchers at IBM recently broke a new record by scanning 10 billion files onto a single data management system in 43 minutes. The technological breakthrough validates the promising future of large-scale storage systems.”
An Algorithm Can Predict Cardiac Arrest 24 Hours Before it Happens - Gizmodo
Predictive Medical Technologies has developed a system that can mine the medical data of a patient—lab reports, monitors, nurse notes, etc.—and predict whether that patient will suffer from cardiac arrest or respiratory failure within 24 hours.
It’s a system that can be integrated into hospitals that are “at a certain technological level” without any new hardware, sampling or extra time. That technological level is rare though, with only 100 US hospitals properly equipped. Bryan Hughes, CEO of Predictive Medical Technologies, explains how it works:
Without giving away too much of our secret sauce, we use non-hypothesis machine learning techniques, which have proven very promising so far. This approach allows us to eliminate any human “expert” bias from the models
Can an Algorithm Spot the Next Google?
A startup analyzes tweets, patents, and lots of other data in the hopes of identifying the next big thing.
By definition, “disruptive” technologies are those that take the world by surprise. Now a startup called Quid claims that its software can make good guesses about what the next big thing will be. It does this by analyzing a store of data on existing companies, ideas, and research.
Over the past 18 months, Quid has developed a system that charts the relationships between existing technologies, and identifies areas ripe for influential new ideas. “The goal is to map the world’s technology and to understand where it’s going,” says Sean Gourley, Quid’s chief technology officer. “The human brain can’t process all of this.” The company thinks its software can help people who invest in early-stage technologies pick more winners than losers, or guide companies into potentially lucrative areas of research.
Source: Technology Review
At this moment, the must-read stories in technology are scattered across hundreds of news sites and blogs. That’s far too much for any reader to follow. Fortunately, Techmeme arranges all of these links into a single, easy-to-scan page. Story selection is accomplished via computer algorithm extended with direct human editorial input. (via About Techmeme)
A powerful computing tool that allows scientists to extract features and patterns from enormously large and complex sets of raw data has been developed by scientists at University of California, Davis, and Lawrence Livermore National Laboratory. The tool - a set of problem-solving calculations known as an algorithm - is compact enough to run on computers with as little as two gigabytes of memory. (via New tool enables powerful data analysis)
