Book Review of Archaeological Theory and the Politics of Cultural Heritage by Laurajane Smith

The passage of the Native American Graves Protection and Repatriation Act (NAGPRA) in 1991 significantly changed the way archaeology would be done in the United States. This act was presaged by growing complaints and resentment directed at the scientific community by Native Americans over the treatment of their ancestral remains. Many of the underlying issues came to a head with the discovery and subsequent court battles over the 9,200-year-old individual commonly known as Kennewick Man. This had a galvanizing effect on the discipline, not only perpetuating the sometimes adversarial relationship between archaeologists and Native Americans, but also creating a rift between those archaeologists who understood Native American concerns and those who saw their ancestral skeletal remains representing the legacy of humankind and thus belonging to everyone. Similar scenarios have emerged in Australia.

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.