Abstract: Resource pooling in ad hoc networks deals with accumulating computing and network resources to implement network control schemes such as routing, congestion, traffic management, and so on. Pooling of resources can be accomplished using the distributed and dynamic nature of ad hoc networks to achieve collaboration between the devices. Ad hoc networks need a resource-pooling technique t...

We view each station in a Jackson network as a queue of tasks, of a particular type, which are to be processed by the associated specialized server. A complete pooling of queues, into a single queue, and servers, into a single server, gives rise to an M/PH/1 queue, where the server is flexible in the sense that it processes all tasks. We assess the value of complete pooling by comparing the ste...

In using pooling designs to identify clones containing a specific subsequence called positive clones, sometimes there exist nonpositive clones which can cancel the effect of positive clones. Various models have been studied which differ in the power of cancellation. Although the various models pose interesting mathematical problems, and ingenious constructions of pooling designs have been propo...

OBJECTIVES Multineuronal spike trains must be efficiently decoded in order to utilize them for controlling artificial limbs and organs. Here we evaluated the efficiency of pooling (averaging) and combining (vectorizing) activities of multiple neurons for decoding neuronal information. METHODS Multineuronal activities in the monkey inferior temporal (IT) cortex were obtained by classifying spi...

We report psychophysical measurements of spatial pooling functions for contrast gain control. We use a nulling technique to measure the dependence of contrast induction on the spatial frequency of a sinusoidal contrast modulation. This dependence on spatial frequency, when transformed, provides the profile of a spatial pooling function. The measured profiles are fitted well by Gaussians. We con...

Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these problems and solve them separately without loss generality. We propose a novel data-pooling algorithm called Shrunken-SAA disproves this intuition. In particular, we prove combining data across outperfo...