Three Independent Evaluations of Healthy Kids Programs Find Dramatic Gains in Well-Being of Children and Families

Presents highlights from evaluations of a comprehensive health insurance coverage program for children, launched by Children's Health Initiatives and supported by the California Endowment, in Los Angeles, San Mateo, and Santa Clara counties

Visual Task Performance Assessment using Complementary and Redundant Information within Fused Imagery

Image fusion is the process of combining information from a set of source images to obtain a single image with more relevant information than any individual source image. The intent of image fusion is to produce a single image that renders a better description of the scene than any of the individual source images. Information within source images can be classified as either redundant or complementary. The relevant amounts of complementary and redundant information within the source images provide an effective metric for quantifying the benefits of image fusion. Two common reasons for using image fusion for a particular task are to increase task reliability or to increase capability. It seems natural to associate reliability with redundancy of information between source bands, whereas increased capability is associated with complementary information between source bands. The basic idea is that the more redundant the information between the source images being fused, the less likely an increase in task performance can be realized using the fused imagery. Intuitively, the benefits of image fusion with regards to task performance are maximized when the source images contain large amounts of complementary information. This research introduces a new performance measure based on mutual information which, under the assumption the fused imagery has been properly prepared for human perception, can be used as a predictor of human task performance using the complementary and redundant information in fused imagery. The ability of human observers to identify targets of interest using fused imagery is evaluated using human perception experiments. In the perception experiments, imagery of the same scenes containing targets of interest, captured in different spectral bands, is fused using various fusion algortihms and shown to human observers for identification. The results of the experiments show a correlation exists between the proposed measure and human visual identification task performance. The perception experiments serve to validate the performance prediction accuracy of the new performance measure. the development of the proposed metric introduces into the image fusion community a new image fusion evaluation measure that has the potential to fill many voids within the image fusion literature

Internet polls are regularly underestimating support for Hillary Clinton

Since the Republican and Democratic conventions in July, Hillary Clinton has experienced a poll ‘bounce’, to lead Donald Trump by about 8 percent, especially in telephone polls with live interviewers. Internet polls, by contrast, tend to show Clinton leading by only 2 or 3 percent. Are these internet polls underestimating Clinton or overestimating Trump? Using results from the 2016 presidential primaries to assess state polls’ accuracy, Taylor Howell, Christopher Stout and Reuben Kline find that that internet polls were slightly more likely to overestimate support for Trump than live interviewer polls, and that they were likely to underestimate support for Clinton by nearly 2 percent. They suggest that online polls may still have some ways to come in terms of accuracy and may be skewing away from Clinton by offering a “don’t know” option to those who are as yet unwilling to commit to voting for her