Many new 3D printers are available, but M3D’s new Micro 3D printer has some very intriguing features we haven’t seen before. The Micro 3D is on a mission: It’s specifically designed to address the burgeoning consumer market, starting with it’s...
Two researchers at Cold Spring Harbor Laboratory challenge the most recent advances in this Big Data analysis, using a classic mathematical concept to tackle the outstanding problems in this field. Mutual information is able to uncover patterns in large lists of numbers, revealing entirely new, unexpected patterns.
Big Data is everywhere, and we are constantly told that it holds the answers to almost any problem we want to solve. Companies collect information on how we shop, doctors and insurance companies gather our medical test results, and governments compile logs of our phone calls and emails. In each instance, the hope is that critical insights are hidden deep within massive amounts of information, just waiting to be discovered.
But simply having lots of data is not the same as understanding it. Increasingly, new mathematical tools are needed to extract meaning from enormous data sets. In work published online today, two researchers at Cold Spring Harbor Laboratory (CSHL) now challenge the most recent advances in this field, using a classic mathematical concept to tackle the outstanding problems in Big Data analysis.
What does it mean to analyze Big Data? A major goal is to find patterns between seemingly unrelated quantities, such as income and cancer rates. Many of the most common statistical tools are only able to detect patterns if the researcher has some expectation about the relationship between the quantities. Part of the lure of Big Data is that it may reveal entirely new, unexpected patterns. Therefore, scientists and researchers have worked to develop statistical methods that will uncover these novel relationships.
In 2011, a distinguished group of researchers from Harvard University published a highly influential paper in the journal Science that advanced just such a tool. But in a paper published today in Proceedings of the National Academy of Sciences, CSHL Quantitative Biology Fellow Justin Kinney and CSHL Assistant Professor Gurinder "Mickey" Atwal demonstrate that this new tool is critically flawed. "Their statistical tool does not have the mathematical properties that were claimed," says Kinney.
Kinney and Atwal show that the correct tool was hiding in plain sight all along. The solution, they say, is a well known mathematical measure called "mutual information," first described in 1948. It was initially used to quantify the amount of information that could be transmitted electronically through a telephone cable; the concept now underlies the design of the world's telecommunications infrastructure. "What we've found in our work is that this same concept can also be used to find patterns in data," Kinney explains. "This beautiful mathematical concept has the potential to greatly benefit modern data analysis, in biology and in biology and many other important fields.
Ya comenté tras uno de los desayunos que organicé el año pasado con Ibercom, que la compañía quería entrar en este negocio de las “Smart cities”. Además, es que en su plan de negocio no habían introducido ningún ...
Página web de la Escuela Politécnica Superior de la Universidad de Alicante || Escola Politècnica Superior d'Alacant (RT @EPSAlicante: Recordatorio: mañana en S.Actos I - fab.U.lab: 1a Jornada de Fabricación Digital
We focused on the common sensors most people wanted and created one solid “chocolate bar” -- breakable, detachable, modular, wireless, cloud-enabled, with SDKs, tutorials, demo apps, etc.Read more on MAKE...
Robonaut 2, el robot-astronauta desarrollado por la NASA, forma parte de un proyecto de investigación en el que se le enseñan procedimientos médicos básicos para que pueda ejercer de médico de futuras misiones espaciales.
The National Institute of Standards and Technology (NIST) today released its first final version of a national framework for improving critical infrastructure cybersecurity. The "living" document will be updated industry reports back on its implementation.