In recent years, content hyper-giants have increasingly deployed server infrastructure and services close to end-users within "eyeball" networks. Still, online streaming analytics has largely remained unaffected by this trend. This is despite the …
Free and open source media centers are experiencing a boom in popularity for the convenience they offer users seeking to remotely consume digital content. Kodi is today’s most popular home media center, with millions of users worldwide. Kodi’s …
The machine learning performance usually could be improved by training with massive data. However, requesters can only select a subset of devices with limited training data to execute federated learning (FL) tasks as a result of their limited budgets …
Optimization acceleration techniques such as momentum play a key role in state-of-the-art machine learning algorithms. Recently, generic vector sequence extrapolation techniques, such as regularized nonlinear acceleration (RNA) of Scieur et al., were …
Evolutionary dynamics on networks are key for biological and social evolution. Typically, the clustering mutants on networks can dramatically alter the direction of selection. Previous studies on the assortment of mutants assume that individuals interact in a frequency-dependent way. It is hard to tell how assortment alone alters the evolutionary fate. We establish a minimal network model to disentangle the assortment from the game interaction. We find that for weak selection limit, the assortment of mutants plays little role in fixation probability. For strong selection limit, connected mutants, i.e., the maximum assortment, are best for fixation. When the mutants are separated by only one wild-type individual, it is worse off than that separated by more than one wild-type individual in fixation probability. Our results show the nontrivial yet fundamental effect of the clustering on fixation. Noteworthily, it has already arisen, even if the game interaction is absent.