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The standards picture in cloud computing is decidedly mixed. On the positive side there are a number of standards-based web technologies that form many of the everyday software components used to build cloud applications. In addition, there are a number of de facto standards (i.e., practices, application program interfaces (APIs), and technologies) that have proven successful for a market leader, and therefore by default form a sort of standard. For example, over the past few years many web-based services provided a standardized means by which other applications could make use of them that was based on a common, easily-used style called a ReST interface (Representational State Transfer see 3, Cloud Computing and Everything Else, for more information). As these services evolved into cloud-based services the ReST interfaces naturally remained, and because they were so easy to use, actually spread through the cloud infrastructures. As a result, ReST-style interfaces have now become the de facto standard for cloud-based applications, platforms, and infrastructures. However, there are few formal, cloud-speci c standards in anything beyond the earliest stages of discussion. Examples here include the Cloud Computing Interoperability Forum (CCIF), NIST, and several others. In practice, the lack of established, formal standards is not a barrier to adoption, at least not at this stage of the industry. However, as standards generally facilitate the interchangeability of suppliers, and therefore reduce the risk for customers and consequently facilitate overall industry adoption and growth, at some point it will be necessary to develop formal, widely-accepted standards. That is why initial discussions have started in several circles.

which shows how the first entry of e r r ~ r relates to the first entry of &. Similarly, for t ea(l) = ~ 1 6 we obtain 0

(6.100)

-to-+,

Alternatively, we can use the NG learning rule (in the Lie Group sense) proposed by Zhang et al. [1363, 1365] (see s 6 and 9 for more details): Wp = [(I f [y(k)] yT (k) ) Wp (1 p0 ) f [y(k)] xT (k p) ] M , (11.40)

19 Distorting Images Using Transformation Effects, Liquify, and Vanishing Point 20 Applying Filters 21 Combining Images

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Evaluate PR firms by asking basic questions: What s the quality of their relationships with the media Can their team help with access to the media representatives you need Who would actually do the work In some firms, senior PR professionals handle client matters for the firm s largest clients, while smaller assignments are handed off to less experienced staff. Understand where you fit in the client pecking order and who will work on your account. How are limits set on scope and costs PR campaigns can be complex, requiring the time of many people. Even with the best intentions, the scope and cost can easily creep beyond your expectations. Who else is out there It s wise to shop thoroughly. Like consulting firms, the number of firms offering PR services is staggering and the quality of service they offer varies widely.

Let us return to the one-dimensional Lagrange nodal elements for a moment again. Assume the reference domain K , = (-1,l) and p 1 disjoint points -1 = y1 < yz < . . . < yp+l = 1. The polynomial space has the form P = PP(K,) and the degrees of freedom are defined as L,(g) = g(y,), f = 1 , 2 , . . . , p 1 for all g E P. Consider another interval K C R connected with K , through the affine reference map (2.37), xe : K , --t I . We construct the Lagrange element (I ,P , 2 ) by defining new nodal points . . , &,+I E K , yz = xe (yz),new polynomial space P ,

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