Risk theory tells us if an insurer can effectively pool a large number of individuals to reduce the total risk, he then can provide the insurance by charging a premium close to the actuarially fair rate. There is, however, a common belief that the risk can be effectively pooled only when the random loss is independent, so that crop insurance markets cannot survive without government subsidy bec...

A pooling space is defined to be a ranked partially ordered set with atomic intervals. We show how to construct non-adaptive pooling designs from a pooling space. Our pooling designs are e-error detecting for some e; moreover e can be chosen to be very large compared with the maximal number of defective items. Eight new classes of nonadaptive pooling designs are given, which are related to the ...

We use agent-based modeling to study osotua, a gift giving system used by the Maasai of East Africa. Osotua’s literal meaning is “umbilical cord,” but it is used metaphorically to refer to a specific type of gift-giving relationship. Osotua relationships are characterized by respect, responsibility and restraint. Osotua partners ask each other for help only if they are in need and provide help ...

Convolutional Neural Network (CNN) is a biologically inspired trainable architecture that can learn invariant features for a number of applications. In general, CNNs consist of alternating convolutional layers, non-linearity layers and feature pooling layers. In this work, a novel feature pooling method, named as mixed pooling, is proposed to regularize CNNs, which replaces the deterministic po...

Not properly accounting for differences between business owners and nonbusiness owners in studies of household wealth can lead to erroneous conclusions about the significance of different saving motives. Using data from the Panel Study of Income Dynamics from the 1980s and 1990s, we show that within samples of both business owners and non– business owners, the amount of precautionary savings wi...

Pooling is one of the key elements in a convolutional neural network. It reduces feature map size, thereby enabling training with limited amount computation. The most common pooling methods are average pooling, max and stride pooling. methods, however, have disadvantage that they can perform only specified fixed functions thus expressive power. In this paper, we propose new method named univers...

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advoc...