A photonic-crystal selective filter

A highly selective filter is designed, working at 1.55 μm and having a 3-dB bandwidth narrower than 0.4 nm, as is required in Dense Wavelength Division Multiplexed systems. Different solutions are proposed, involving photonic crystals made rectangular- or circular-section dielectric rods, or else of holes drilled in a dielectric bulk. The polarization and frequency selective properties are achieved by introducing a defect in the periodic structure. The device is studied by us- ing in-house codes implementing the full-wave Fourier Modal Method. Practical guidelines about advantages and limits of the investigated solutions are given

State Lotteries and Consumer Behavior

Despite considerable controversy surrounding the use of state lotteries as a means of public finance, little is known about their consumer consequences. This project investigates two central questions about lotteries. First, do state lotteries primarily crowd out other forms of gambling, or do they crowd out non-gambling consumption? Second, does consumer demand for lottery games respond to expected returns, as maximizing behavior predicts, or do consumers appear to be misinformed about the risks and returns of lottery gambles? Analyses of multiple sources of micro-level gambling data demonstrate that lottery spending does not substitute for other forms of gambling. Household consumption data suggest that household lottery gambling crowds out approximately $38 per month, or two percent, of other household consumption, with larger proportional reductions among low-income households. Demand for lottery products responds positively to the expected value of the gamble, controlling for other moments of the gamble and product characteristics; this suggests that consumers of lottery products are not simply uninformed, but are perhaps making fully-informed purchases.

Evaluating color texture descriptors under large variations of controlled lighting conditions

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.Comment: Submitted to the Journal of the Optical Society of America

Color Constancy Using CNNs

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR 2015 workshop

Income Inequality and Early Non-Marital Childbearing: An Economic Exploration of the "Culture of Despair"

Using individual-level data from the United States and a number of other developed countries, we empirically investigate the role of income inequality in determining rates of early, non-marital childbearing among low socioeconomic status (SES) women. We present robust evidence that low SES women are more likely to give birth at a young age and outside of marriage when they live in more unequal places, all else held constant. Our results suggest that inequality itself, as opposed to other correlated geographic factors, drives this relationship. We calculate that differences in the level of inequality are able to explain a sizeable share of the geographic variation in teen fertility rates both across U.S. states and across developed countries. We propose a model of economic “despair” that facilitates the interpretation of our results. It reinterprets the sociological and ethnographic literature that emphasizes the role of economic marginalization and hopelessness into a parsimonious framework that captures the concept of “despair” with an individual’s perception of economic success. Our empirical results are consistent with the idea that income inequality heightens a sense of economic despair among those at the bottom of the distribution.

House Prices and Birth Rates: The Impact of the Real Estate Market on the Decision to Have a Baby

This project investigates how changes in Metropolitan Statistical Area (MSA)- level housing prices affect household fertility decisions. Recognizing that housing is a major cost associated with child rearing, and assuming that children are normal goods, we hypothesize that an increase in real estate prices will have a negative price effect on current period fertility. This applies to both potential first-time homeowners and current homeowners who might upgrade to a bigger house with the addition of a child. On the other hand, for current homeowners, an increase in MSA-level house prices will increase home equity, leading to a positive effect on birth rates. Controlling for MSA fixed effects, trends, and time-varying conditions, our analysis finds that indeed, short-term increases in house prices lead to a decline in births among non-owners and a net increase among owners. Our estimates suggest that a $10,000 increase in house prices leads to a 2.1 percent increase in births among home owners, and a 0.4 percent decrease among non-owners. At the mean U.S. home ownership rate, our estimates imply that the net effect of a$10,000 increase in house prices is a 0.8 percent increase in births. Given underlying differences in home ownership rates, the predicted net effect of house price changes varies across demographic groups. Our paper provides evidence that homeowners use some of their increased housing wealth, coming from increases in local area house prices, to fund their childbearing goals. In addition, we find that changes in house prices exert a larger effect on current period birth rates than do changes in unemployment rates.