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n To share your Smart Filter with another layer, first convert the target layer to a Smart Object (choose Filter Convert for Smart Filters, or right-click the layer and choose the same command from the pop-up menu), and then press the Alt key (or the Option key on the Mac) and drag the existing Smart Filters layer from its current layer to the target layer. It remains in effect on the current layer and is applied to the target layer, after a progress bar appears, informing you which Smart Filter is being applied. n To move your Smart Filter to another Smart Object (another layer that s been converted for Smart Filters), simply drag it. It leaves the layer it was applied to and is applied to the target layer again, after a progress bar and notice of which Smart Filter is underway. n If you want to apply your Smart Filters in a different order with their current layer say applying Add Noise before you apply Watercolor drag the Smart Filters within their current stack underneath the layer to which they re applied. This is just like rearranging layers within an image.

Now, in order to identify the entries { x ,y, Z} in the post-array (37.19), i t . , in the equality below

The information measures S( /z), where = {X, Y } is the set of classes and z a point in the space, express a measure of separation or discrimination between the classes X and Y at point z. These information measures also depend on: 1. The a priori probabilities Pr(X), Pr(Y ) of the classes X and Y 2. The conditional probability density functions p(z/X), p(z/Y ) 3. The a posteriori probabilities p(X/z) and p(Y /z) of the classes, conditional on z 4. The combined probability density p(z) = Pr(X)p(z/X) + Pr(Y )p(z/Y ) The following is the a list of major separation measures: Shannon entropy S( /z) = E{ Pr(X/z) log Pr(X/z) Pr(Y /z) log Pr(Y /z)} Average quadratic entropy S( /z) = E{Pr(X/z)[1 Pr(X/z)] + Pr(Y /z)[1 Pr(Y /z)]} Bayesian distance S( /z) = E{Pr(X/z)2 + Pr(Y /z)2 } Kullback-Leibler divergence S( /z) = E Bhattacharyya coe cient S( /z) = log( ), where = [p(X/z)p(Y /z)] 2 dx, 0 < < 1.

Protect your Briefing Book: have objective and data owners enter their text comments on a separate sheet that links to the word-wrapped cells in the Briefing Book.

After you create text with the Rolling Title tool, you can specify the speed and direction of the animated text using the Rolling Title Options dialog box. In this dialog box, you can also choose how many frames the text is to remain motionless before or after the animated text effect. To open the Rolling Title Options dialog box (shown in Figure 8-6), select the Rolling Title tool.

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However, studio strobelights flash at anywhere between 1/750 and 1/10,000 of a second When the strobes fire, you want your entire image sensor exposed So, you need to know the x-sync speed of the camera the fastest shutter speed at which the full sensor is exposed long enough to capture all the light of the strobe Shoot any faster, and you ll light only part of the picture The faster you shoot, the narrower the picture frame will be On the other hand, you don t want to set the shutter any slower That s because you could pick up ghost images that occur when your shutter remains open after the flash has fired, if there s enough ambient light to illuminate the subject (Unless you want to use blur as part of your composition) You also will have to set your camera s f-stop manually.

for the friction matrix, we would like to take r large, but then we are faced with two problems: (1) the separation of time scales may not be sufficiently pronounced, and (2) the evaluation of (8.77) in simulationsleads to large error bars. As a consequence of the first problem, the slow variables begin to change in the time range considered in the integral (8.77), and one obtains corrections depending on the ratio of fast to slow time scales.39 If one would like to consider large r , then one should modify the slow time evolution by a control mechanism that artificially keeps z constant, where the effect of the control mechanism should be kept of the same order as the unavoidable corrections, depending on the ratio of fast to slow time scales. The best known control mechanisms of this type are the thermostats for maintaining a constant temperature (for generalizations, see Section 8.4.3). Before we discuss the second problem, that is, the size of the statistical errors for large r, we derive some alternative versions of the Green-Kubo expression (8.77). By assuming that the system is invariant under time translations and that we know its bare Onsager-Casimir symmetry, we obtain