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Geometric algorithms that involve projections of the network onto simpler geometric objects, such as the line A representative of such methods is Linear_Projection [11], in which the positions of the nodes are projected onto a line, and then an optimal distance-2 coloring is computed for those projected points One effect of projecting nodes onto a line is that the projections of nodes may now be within distance-2, whereas the original nodes were not within distance-2 The algorithm selects a line for projection that minimizes the number of such false distance-2 neighbors Linear_Projection runs in time O(n2) on arbitrary graphs There are no results on the approximation ratio 3 Dynamic greedy methods that also color nodes in a greedy fashion, but in which the order of the coloring is determined dynamically as the coloring proceeds A representative of such methods is max_cont_color [16].

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fundamental data, and empirical strategies that had been developed by physiology, since ultimately humans are simply physiological machines It was in this context that Wundt developed the subtractive method to measure mental function An example of how the subtractive method works would be to rst measure the reaction time for a simple task, say by tapping a key at the onset of a light (call this Ts) Next the observer is given a more complex task, say one in which he had to make a decision as to whether the light was red or green, tapping a key with his right hand for red and with his left hand for green (call this Tc).

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This algorithm initially colors an arbitrary node and then all of the one-hop neighbors of that node At each subsequent step, the algorithm chooses for coloring a node that is now most constrained by its distance-2 neighbors Results [16, 18] show that this algorithm has the best simulation performance among all existing broadcast scheduling algorithms A careful implementation [18] yields a running time of O(nD) on arbitrary graphs and O(n ) on unit disk graphs The algorithm is -approximate on arbitrary graphs, and 13-approximate on unit disk graphs [18] 1642 A Better Approximation Ratio The best approximation ratio for arbitrary graphs of the methods cited above is min(n1/2, ), which is also the ratio of the simplest of these algorithms, P_Greedy Below, an algorithm of the traditional greedy variety is described that has an arguably stronger ratio for most graphs.

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Since the more complex task takes more mental computation, Tc is longer than Tn, and Wundt reasoned that the actual time that the decisional process takes, Td, could be computed by the simple subtraction Td Tc Ts This should give the researcher a metric Reaction time should increase in direct proportion to the dif culty of the decision or the number of decisions that had to be made Although this methodology generated a lot of research, concerns began to be expressed by some researchers N Lange, working in Wundt s lab, found that attentional processes affected the length of the reaction time Unattended or unexpected stimuli took longer to respond to, and paying attention to the response rather than to the stimulus also altered the reaction time.

The algorithm is similar to Static_min_deg_last but the nodes are ordered in a dynamic, rather than static, fashion The term progressive is taken from [23] Algorithm progressive_min_deg_last(G) Labeler(G, n); for j 1 to n do.

Other researchers, such as Oswald Kl pe (1862 1915), suggested that the method was not valid because the entire perceptual act is not simply the sum of simple sensory and decision times Returning to the example above, suppose that we compare the time that it takes to detect a light (Ts ) to the time that it takes to discern the locus of lights (eg, whether a pair of lights were side by side or one above the other Tl ); now, following this decision we will also require the observer to add the color discrimination task that we described earlier (Tc ) The addition of a second mental operation or sensory input was known as the complication method.

As we discussed, sometimes you want to nest elements within other elements. For example, you may want to italicize something that occurs in a paragraph. You ve seen the paragraph element, P, in all our examples. The italics element is I. In the previous code example (see Making Your HTML Readable ), the words Over the Web Stay in Touch are italicized within a paragraph. Many valid types of nesting exist. For example, nearly any element you put within the BODY element is valid, except for the HEAD element. You can t nest some elements, such as a link inside a link.

1 Proof: Let u(x) = |X | be the uniform probability mass function over X, and let p(x) be the probability mass function for X. Then

One of my principal interests in psychology to which I have returned several times has been the study of personality. . . . My con ict here was that, on the one hand, the center of psychology probably was the study of personality, but, on the other hand, I was unable to invent any experimental leverage in this eld. That was the reason why I turned to other problems that seemed to lend themselves to more rigorous analysis. (1952, p. 318)

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