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3. Spatial or temporal autocorrelation, meaning that two uncertainties depend on the spatial or temporal realization of some third uncertainty which itself exhibits stochastic dependence in space or time. The performances of two sections of a long levee may depend on soil engineering properties in the naturally occurring valley bottom, which, when modeled as a stochastic (aleatory) process, exhibit a long wave length of correlation in space; thus adjacent sections will exhibit similar settlements or factors of safety against slope instability. 4. Statistical correlation, meaning that two uncertainties are simultaneously estimated from a xed set of data and therefore in uenced by a common sampling variability error. In soil mechanics, a common and almost always overlooked statistical correlation is that between soil cohesion, c, and soil friction angle, , which, being regression parameters of the Mohr-Coulomb model, are negatively correlated, given a nite number of test data.

Summary

2, where the best CCR for each L is highlighted with bold fonts From the detailed results, it can be seen that the rst a few features extracted by the UMLDA algorithm consistently outperforms all the other algorithms, although the number of useful features extracted by UMLDA is limited compared to other MLDA variants [30] In contrast, the heuristic TR1DA algorithm, built upon a greedy approximation approach, performs the worst in most cases, especially when the number of samples per subject is small (eg, L = 5, 10) Similarly, the DATER algorithm outperforms the GTDA greatly on the PIE database Thus, the MLDA variants based on scatter ratio have achieved much better results than the MLDA variants based on the scatter difference in this experiment, with the added bene t that there is no need to choose a tuning parameter .

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ColdFusion offers a wide range of exception handling techniques, from CFTRY and CFCATCH to the all-encompassing site-wide error handler. Each technique can be powerful by itself, but only when these three are intelligently integrated do you reach the full potential of ColdFusion s exception handling framework. In this chapter you learn about the different ways to use ColdFusion s exception-handling framework and how the different pieces work together. You also learn about the many types of errors that can be thrown and caught with CFCATCH and CFERROR, and you even learn how to create your own custom error types that you can use for your own purposes.

For the comparison between MLDA-TTP and MLDA-TVP, we focus on the scatter ratio-based variants: UMLDA and DATER As mentioned above, the most discriminative features extracted by UMLDA seem to outperform the most discriminative.

22 Understanding ColdFusion MX Components 23 Building Your First ColdFusion Components 24 Migrating Custom Tags and UDFs to Components 25 Building Web Services with ColdFusion MX 26 Flash Remoting Services 27 Using Server-Side ActionScript

omponents are perhaps the most eagerly anticipated new feature of ColdFusion MX and for good reason. Components not only offer a new and better way to write ColdFusion code, but they also enable ColdFusion to expose its functionality to the outside world through Web services and also make developing Flash MX applications a snap. Components bring a more object-like approach to ColdFusion, so be prepared to learn some new coding techniques. In fact, if ColdFusion is your first and only language, this object-like approach to ColdFusion programming may seem very odd and cumbersome at first, but if you stay with us through this chapter, you see a faster and more logical way to write maintainable applications.

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Figure 20.22 shows the event tree for levee failure during an extreme ood that was developed earlier, but now with branch probabilities. Table 20.5 shows the justi cation for each probability estimate, the estimation approach, and the sources of uncertainty due to natural variation and limited knowledge.

Note This chapter assumes that you have read about and are familiar with creating user-defined functions by using CFFUNCTION, CFARGUMENT, and CFRETURN tags. If not, start by reading 17.

Using components to separate logic from presentation Structuring components Invoking component functions in various ways Understanding both static and persistent components Implementing advanced component techniques

Figure 2.8. Detailed face recognition results on the CMU PIE database with (a) L = 5, (b) L = 10,

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