prostheticknowledge:

An algorithm for tracking viruses (and Twitter rumors) to their source 
Scientific proposal for sourcing origins of viral information, from tweets and blogs to diseases (such as through a river network, pictured above) - via Gigaom:

No, Vanilla Ice isn’t dead — and if he had access to a new algorithm from Swiss researcher Pedro Pinto, the Ice Man could go all techno-ninja and track down who started the rumor claiming he was. That’s because Pinto and his colleagues at the Ecole Polytechnique Fédérale de Lausanne have developed an algorithm for finding the source of such rumors, as well as viruses (physical and digital) and other maladies, even across highly complex networks.
Their method, according to an abstract of a paper just published in Physical Review Letters, is ideal for situations where there is relatively little data to work with, and is “based on the principles used by telecommunication towers to pinpoint cell phone users.” Essentially, the algorithm starts by looking at a small collection of points within a network and working back from there to determine the origin, kind of like how investigators can zero in on a cell phone’s location using triangulation. The more connections, or observers, a particular point has, the fewer that are needed to track down the source point.
… Pinto explains that his team’s method could also be used for everything from identifying the source of a computer virus to determining the blogs most likely to make web content go viral to preventing the spread of an epidemic or chemical attack by learning how it’s spreading.

More Here

prostheticknowledge:

An algorithm for tracking viruses (and Twitter rumors) to their source 

Scientific proposal for sourcing origins of viral information, from tweets and blogs to diseases (such as through a river network, pictured above) - via Gigaom:

No, Vanilla Ice isn’t dead — and if he had access to a new algorithm from Swiss researcher Pedro Pinto, the Ice Man could go all techno-ninja and track down who started the rumor claiming he was. That’s because Pinto and his colleagues at the Ecole Polytechnique Fédérale de Lausanne have developed an algorithm for finding the source of such rumors, as well as viruses (physical and digital) and other maladies, even across highly complex networks.

Their method, according to an abstract of a paper just published in Physical Review Letters, is ideal for situations where there is relatively little data to work with, and is “based on the principles used by telecommunication towers to pinpoint cell phone users.” Essentially, the algorithm starts by looking at a small collection of points within a network and working back from there to determine the origin, kind of like how investigators can zero in on a cell phone’s location using triangulation. The more connections, or observers, a particular point has, the fewer that are needed to track down the source point.

… Pinto explains that his team’s method could also be used for everything from identifying the source of a computer virus to determining the blogs most likely to make web content go viral to preventing the spread of an epidemic or chemical attack by learning how it’s spreading.

More Here

matthen:

Some people supposed that the intricate pattern generated from Astroids which I posted could be a fractal. This animations zooms in to the top of the image, showing it is infinitely detailed and self-similar on smaller scales.

Mathen, continuously posting awesome informative gifs that blow my mind. Follow him!

matthen:

Some people supposed that the intricate pattern generated from Astroids which I posted could be a fractal. This animations zooms in to the top of the image, showing it is infinitely detailed and self-similar on smaller scales.

Mathen, continuously posting awesome informative gifs that blow my mind. Follow him!

matthen:

If you roll a circle inside one 3 times its size, it will actually trace out a 4 pointed star shape called an Astroid (this shape is traced out in the animation in orange).  But what if inside the smaller circle, there is an even smaller one tracing out a smaller Astroid?  This animation shows the intricate shape that is generated by adding the effects of all the Astroids.  [code] [also]

matthen:

If you roll a circle inside one 3 times its size, it will actually trace out a 4 pointed star shape called an Astroid (this shape is traced out in the animation in orange).  But what if inside the smaller circle, there is an even smaller one tracing out a smaller Astroid?  This animation shows the intricate shape that is generated by adding the effects of all the Astroids.  [code] [also]

matthen:

Chaos Theory is a very important area of mathematics which can explain a lot of what we see in the real world.  A pendulum with one mass is relatively easy to explain mathematically, and it behaves nicely. However if you put another mass in there, it behaves chaotically. Technically, this means that if you change the starting positions only slightly, the state of the system a short time later can change drastically.  The weather is chaotic- a small error in measuring it today could be the difference between rain and no rain in a weeks days time. Watch these two pendulum systems quickly diverge, though they both start off with nearly the same settings. [more] [code]

matthen:

Chaos Theory is a very important area of mathematics which can explain a lot of what we see in the real world.  A pendulum with one mass is relatively easy to explain mathematically, and it behaves nicely. However if you put another mass in there, it behaves chaotically. Technically, this means that if you change the starting positions only slightly, the state of the system a short time later can change drastically.  The weather is chaotic- a small error in measuring it today could be the difference between rain and no rain in a weeks days time. Watch these two pendulum systems quickly diverge, though they both start off with nearly the same settings. [more] [code]

matthen:

Can you visualise how big a million really is?  Imagine a picture made of 10 rows of 10 points, giving 100 points in total. Then take that picture, and reproduce it in 10 rows of 10 copies. The big picture would now have 10,000 points in it. Then do that again! To fit the points into a small image, they are now around 15 points in an individual pixel on your screen. If you did this scaling out three more times, you would get to a trillion (1,000,000,000,000). [more] [code for Mathematica animation]

matthen:

Can you visualise how big a million really is?  Imagine a picture made of 10 rows of 10 points, giving 100 points in total. Then take that picture, and reproduce it in 10 rows of 10 copies. The big picture would now have 10,000 points in it. Then do that again! To fit the points into a small image, they are now around 15 points in an individual pixel on your screen. If you did this scaling out three more times, you would get to a trillion (1,000,000,000,000). [more] [code for Mathematica animation]