The Sparse Inference algorithm sounds too good to be true, since it appears to be a remedy for what the researchers term a "seemingly impossible task." But the heart of the story is that with a careful selection process, all possible nodes of a network do not have to be evaluated in order to make accurate inferences. The real work, however, lies ahead for these researchers, as they try to use Sparse Inference to make predictions about unknown source-locations that actually pan out. For that, they will need to refine a methodology for picking those key nodes and prove that accurate predictions were actually made from their selection. To read a their paper (for free) try:
with some supplemental material here:
An interesting concept: I suppose it is the converse of the old expression "you can't get there from here". Knowing which nodes first encounter a threat means that certain paths in a network are likely and other ones are ruled out. Certain source locations become prime candidates for further investigation.
As we unveil EE Times’ 2015 Silicon 60 list, journalist & Silicon 60 researcher Peter Clarke hosts a conversation on startups in the electronics industry. Panelists Dan Armbrust (investment firm Silicon Catalyst), Andrew Kau (venture capital firm Walden International), and Stan Boland (successful serial entrepreneur, former CEO of Neul, Icera) join in the live debate.