Incorporating scale invariance into the cellular associative neural network

This paper describes an improvement to the Cellular Associative Neural Network, an architecture based on the distributed model of a cellular automaton, allowing it to perform scale invariant pattern matching. The use of tensor products and superposition of patterns allows the system to recall patterns at multiple resolutions simultaneously. Our experimental results show that the architecture is capable of scale invariant pattern matching, but that further investigation is needed to reduce the distortion introduced by image scaling

Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History

_Background:_ We assessed the clinical utility of a first-degree breast cancer family history and polygenic risk score (PRS) to inform screening decisions among women aged 30-50 years. _Methods:_ Two established breast cancer models evaluated digital mammography screening strategies in the 1985 US birth cohort by risk groups defined by family history and PRS based on 313 single nucleotide polymorphisms. Strategies varied in initiation age and interval. The benefits and harms were compared with those seen with 3 established screening guidelines. _Results:_ Women with a breast cancer family history who initiated biennial screening at age 40 years had a 36% increase in life-years gained and 20% more breast cancer deaths averted, but 21% more overdiagnoses and 63% more false positives. Screening tailored to PRS vs biennial screening from50 to 74 years had smaller positive effects on life-years gained and breast cancer deaths averted but also smaller increases in overdiagnoses and false positives. Combined use of family history and PRS vs biennial screening from 50 to 74 years had the greatest increase in life-years gained and breast cancer deaths averted. _Conclusions:_ Our results suggest that breast cancer family history and PRS could guide screening decisions before age 50 years among women at increased risk for breast cancer but expected increases in overdiagnoses and false positives should be expected

Improved visibility computation on massive grid terrains

This paper describes the design and engineering of algorithms for computing visibility maps on massive grid terrains. Given a terrain T, specified by the elevations of points in a regular grid, and given a viewpoint v, the visibility map or viewshed of v is the set of grid points of T that are visible from v. We describe three new algorithms to compute the viewshed for any given terrain T and viewpoint v. The first two algorithms "sweep" the terrain by rotating a ray around the viewpoint while maintaining the terrain profile along the ray. On a terrain of n grid points, these algorithms run in O(n log n) time and O(sort(n)) I/Os in the I/O-model of Aggarwal and Vitter. The difference between the two algorithms is in the preprocessing before the sweep: the first algorithm sorts the grid points into concentric bands around the viewpoint; the second algorithm sorts the grid points into sectors around the viewpoint. The third algorithm sweeps the terrain centrifugally, growing a star-shaped region around the viewpoint while maintaining the approximate visible horizon of the terrain within the swept region. This algorithm runs in O(n) time and O(scan(n)) I/Os and is cache-oblivious. We tested our algorithms on NASA SRTM data, and found that our fastest new algorithm computes the viewshed of a terrain of 7.6 billion points (28.4 GiB) in 203 minutes on a machine with 0.5 GiB RAM and a laptop-speed hard drive. Depending on the data set, the new algorithm is 20 to 50 times faster than the algorithm from our previous work