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Single file NIfTI format, used extensively in biomedical imaging. 6E 69 31 00: ni1: 344 hdr Header file of a .hdr/.img pair in NIfTI format, used extensively in biomedical imaging. 52 41 46 36 34: RAF64: 0 Report Builder file from Digital Metaphors: 56 49 53 33: VIS3: 0 Resource file Visionaire 3.x Engine: 4D 53 48 7C 42 53 48 7C: MSH| BSH| 0 hl7
The median of a finite list of numbers is the "middle" number, when those numbers are listed in order from smallest to greatest. If the data set has an odd number of observations, the middle one is selected (after arranging in ascending order). For example, the following list of seven numbers, 1, 3, 3, 6, 7, 8, 9
Firstly, computing median of an odd list is faster and simpler; while one could use an even list, this requires taking the average of the two middle elements, which is slower than simply selecting the single exact middle element. Secondly, five is the smallest odd number such that median of medians works.
The weighted median can be computed by sorting the set of numbers and finding the smallest set of numbers which sum to half the weight of the total weight. This algorithm takes () time. There is a better approach to find the weighted median using a modified selection algorithm. [1]
Taking the mean μ of X to be 0, the median of Y will be 1, independent of the standard deviation σ of X. This is so because X has a symmetric distribution, so its median is also 0. The transformation from X to Y is monotonic, and so we find the median e 0 = 1 for Y. When X has standard deviation σ = 0.25, the distribution of Y is weakly skewed.
() operations, which force us to visit every node in ascending order (such as printing the entire list), provide the opportunity to perform a behind-the-scenes derandomization of the level structure of the skip-list in an optimal way, bringing the skip list to () search time. (Choose the level of the i'th finite node to be 1 plus the number ...
This relates directly to the k-median problem which is the problem of finding k centers such that the clusters formed by them are the most compact with respect to the 2-norm. Formally, given a set of data points x , the k centers c i are to be chosen so as to minimize the sum of the distances from each x to the nearest c i .
A further relaxation requiring only a list of the k smallest elements, but without requiring that these be ordered, makes the problem equivalent to partition-based selection; the original partial sorting problem can be solved by such a selection algorithm to obtain an array where the first k elements are the k smallest, and sorting these, at a total cost of O(n + k log k) operations.