So here’s an interesting question: is Uber cheaper than conventional taxis? Today, we get an answer thanks to the work of Cecilia Mascolo at the University of Cambridge in the U.K. and a few pals who have compared Uber’s prices with those of Yellow Taxis in New York City. They say the ability to compare prices in this way should help commuters choose the cheapest options and thereby help control costs for all cab users.
The team’s method is possible because of a 2014 freedom of information request for the data associated with New York City Yellow Taxi journeys during the whole of 2013.
This data set is vast, covering hundreds of millions of trips and tipping the scales at 50 gigabytes. It consists of the location of every pick up and drop off as well as the fare paid for each journey.
Comparing it with Uber’s price at any instant is straightforward. Mascolo and co took the coӧrdinates of each journey made in a Yellow Taxi in 2013 and then asked Uber how much it would charge for the same journey using the cheapest version of the service, called Uber X.
Uber then suggested a minimum and maximum possible fare, which Mascolo and co used to take an average. They then compared this figure against the Yellow Taxi fare.
The results make for interesting reading. “Uber appears more expensive for prices below 35 dollars and begins to become cheaper only after that threshold,” say Mascolo and co.
The Ebola virus has consistently stayed several steps ahead of doctors, public officials and others trying to fight the epidemic. Throughout the first half of 2014, it spread quickly as international and even local leaders failed to recognize the severity of the situation. In recent weeks, with international response in high gear, the virus has thrown more curve balls.
The spread has significantly slowed in Liberia and beds for Ebola patients are empty even as the U.S. is building multiple treatment centers there. Meanwhile the epidemic has escalated greatly in Sierra Leone, which has a serious dearth of treatment centers. And in Mali, where an incursion was successfully contained in October, a rash of new cases has spread from an infected imam.
Predicting the trajectory of Ebola rather than playing catching-up could do much to help prevent and contain the disease. Some experts have called for prioritizing mobile treatment units that can be quickly relocated to the spots most needed. Figuring out where Ebola is likely to strike next or finding emerging hot spots early on would be key to the placement of these treatment centers.
But such modeling requires data, and lots of it.
Read on here
IN MAY last year, a supercomputer in San Jose, California, read 100,000 research papers in 2 hours. It found completely new biology hidden in the data. Called KnIT, the computer is one of a handful of systems pushing back the frontiers of knowledge without human help.
KnIT didn’t read the papers like a scientist – that would have taken a lifetime. Instead, it scanned for information on a protein called p53, and a class of enzymes that can interact with it, called kinases. Also known as “the guardian of the genome”, p53 suppresses tumours in humans. KnIT trawled the literature searching for links that imply undiscovered p53 kinases, which could provide routes to new cancer drugs.
Having analysed papers up until 2003, KnIT identified seven of the nine kinases discovered over the subsequent 10 years. More importantly, it also found what appeared to be two p53 kinases unknown to science. Initial lab tests confirmed the findings, although the team wants to repeat the experiment to be sure.
KnIT is a collaboration between IBM and Baylor College of Medicine in Houston, Texas. It is the latest step into a weird world where autonomous machines make discoveries that are beyond scientists, simply by rifling more thoroughly through what we already know, and faster than any human can.
Read on: @New Scientist