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where C represents a constant not involving the parameters This same approach can be applied to the SDM model by simply de ning X = [X W X] in (96) The SEM model has a concentrated log likelihood taking the form ln L = C + ln|In W | (n/2) ln(e e) X = X W X y = y Wy = (X X) 1 Xy e = y X The computationally troublesome aspect of this is the need to compute the log determinant of the n n matrix (In W ) Operation counts for computing this determinant grow with the cube of n for dense matrices While W is a n n matrix, in typical problems W will be sparse.



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For a spatial weight matrix constructed using Delaunay triangles between n points in two dimensions, the average number of neighbors for each observation will equal 6, so the matrix will have 6n nonzeros out of n2 possible elements, leading to 6/n as the proportion of nonzeros 931 Sparse Matrix Algorithms A sparse matrix is one that contains a large proportion of zeros As a concrete example, consider the spatial weight matrix for a sample of 3107 US counties used in Pace and Barry (1997) This matrix is sparse since the largest number of neighbors to any county is eight and the average number of neighbors is four To understand how sparse matrix algorithms conserve on storage space and computer memory, consider that we only need record the nonzero elements along with an indication of their row and column position.





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Studies of nonimpaired readers indicate that many of the processes involved with reading, such as perception of single sounds and retrieval of phonemes from memory (Bradley & Bryant, 1983; Torgesen, Wagner, & Rashotte, 1994; R K Wagner et al, 1997), have speci c neural substrates (Cao, Bitan, Chou, Burman, & Booth, 2006; Dufour, Serniclaes, Sprenger-Charolles, & D monet, 2007; Richards, Aylward, Field et al, 2006) These substrates are summarized in Table 21 Similarly, research consistently indicates patterns of structural anomalies and functional differences in children and adults with dyslexia For example, both adults and children with dyslexia consistently have been found to have smaller regions in language areas (eg, left planum, bilateral insular regions, and right anterior region), resulting in exaggerated asymmetry (C M Leonard et al, 2002; Par -Blagoev, 2007).

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Wireless protocols: A standardized wireless protocol will be required to realize the vision of a fully pervasive computing future Today, three different broadcast protocols are in use across the United States to deliver digital information to handsets This means that a cell phone that works f ine in California may not operate in Oklahoma, and that signal coverage can vary greatly across the country or across the city depending on the kind of device and the existing business arrangements between carriers All of Europe and much of Asia operate with a single wireless protocol, the Global System for Mobile communications, or GSM It s no surprise that both Asia and Europe have far more advanced wireless networks than North America, and that so-called 3G or Third Generation features like wireless e-mail and Internet are much more common there than in the United States and Canada.

This requires a 1 3 vector for each nonzero element, consisting of a row index, a column index, and the element value Since nonzero elements represent a small fraction of the total 3107 3107 = 9,653,449 elements in the weight matrix, we save on computer memory For our example of the 3107 US counties, only 12,429 nonzero elements were found in the weight matrix, representing a very small fraction (about 04%) of the total elements Storing the matrix in sparse form requires only three times 12,429 elements, or more that 250 times less computer memory than would be needed to store 9,653,449 elements In addition to storage savings, sparse matrices result in lower operation counts as well, speeding computations.

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