Part V Advanced Programming Techniques in Java

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Table 7.2. Pig Adipose Tissues: Composition and Technological Properties Jowl fat Water content (%) Protein content (%) Fat content (%) Saturated fatty acids (% fat content) 15 20 [5] 10 15 [5] 70 75 [5] 34.3 [2] 35.1 [2] Belly fat Ham fat 11 14 [3] 6 7 [3] 79 80 [3] Backfat 10 [3] 6 7 [3] 81.3 83 [3] 39.3 [1] 36.0 [2] 41.4 43.1 [3] 38.9 [6] 32.3 36.9 [7] C12:0 trace [1] C14:0 1.60 [1] C16:0 23.7 [1] C16:0 24.8 [6] C18:0 15.0 [1] C18:0 13.7 [3] C18:0 12.2 [6] 39.7 [1] 48.0 [2] 46.9 47.9 [3] 40.3 [6] 52.5 57.3 [7] C16:1 4.0 [1] C17:1 trace [1] C18:1 34.7 [1] C18:1 n-9 36.8 [6] Kidney fat 7.5 [3] 3.5 [3] 89 [3] 52.5 [3] 49.5 [6] C18:0 14.5 [3] C16:0 28.8 [6] C18:0 19.4 [3] C18:0 18.6 [6] 40.2 [3] 36.1 [6] Mono-unsaturated fatty acids (% fat content) 49.9 [2] 49.1 [2] C18:1 n-9 33.6 [6]
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In the RnS approach, a repeater consists only of a receiving antenna, an amplifying chain, and a transmitting antenna. In this case, the signals from all the
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Now you have a reference to an array of hashes containing the contents of the Inbox. You can loop through this array of hashes, and pull out the details of the messages with the et_indv_email function. This function can either take the message ID or, as in this case, take the reference to the specific message, like this:
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Extraction method: principal axis factoring; two factors extracted, 152 iterations required. Factor loadings are much weaker than for the preceding example.
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Flash can generate an image map for a GIF to maintain URL links for buttons in the original movie. Place the frame label #Map in the keyframe in which you want to create the image map. If you don t create a frame label, Flash creates an image map using the buttons in the last frame of the movie. You can create an image map only if the $IM template variable is present in the template you select. See Creating an image map on page 385.
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Figure 6.1 Highly connected group versus loosely connected, hub-like group.
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We also de ne the conditional entropy of a random variable given another as the expected value of the entropies of the conditional distributions, averaged over the conditioning random variable. De nition If (X, Y ) p(x, y), the conditional entropy H (Y |X) is de ned as H (Y |X) =
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to a size that could easily be interpreted. Also, retaining too many variables may lead to over tting, in which the generality of the ndings is hindered because the new data do not behave the same as the training data for all the variables. Further, analysis solely at the variable level might miss the fundamental underlying relationships among predictors. For example, several predictors might fall naturally into a single group (a factor or a component) that addresses a single aspect of the data. For example, the variables savings account balance, checking accountbalance, home equity, stock portfolio value, and 401K balance might all fall together under the single component, assets. In some applications, such as image analysis, retaining full dimensionality would make most problems intractable. For example, a face classi cation system based on 256 256 pixel images could potentially require vectors of dimension 65,536. Humans are endowed innately with visual pattern recognition abilities, which enable us in an intuitive manner to discern patterns in graphic images at a glance, patterns that might elude us if presented algebraically or textually. However, even the most advanced data visualization techniques do not go much beyond ve dimensions. How, then, can we hope to visualize the relationship among the hundreds of variables in our massive data sets Dimension reduction methods have the goal of using the correlation structure among the predictor variables to accomplish the following: r To reduce the number of predictor components r To help ensure that these components are independent r To provide a framework for interpretability of the results In this chapter we examine the following dimension reduction methods: r Principal components analysis r Factor analysis r User-de ned composites This chapter calls upon knowledge of matrix algebra. For those of you whose matrix algebra may be rusty, see the book series Web site for review resources. We shall apply all of the following terminology and notation in terms of a concrete example, using real-world data.
formulations to help assess reliability. Later, they will also test reliability of adherence assessments of Utah IRT therapists. Some outcome measures include pre-post structured interviews and psychometrics (e.g., Structured Clinical Interview for DSM-IV, Axis I [SCID-I; First, Spitzer, Gibbon, & Williams, 1995]; Structured Clinical Interview for DSM-IV, Axis II [SCID-II; First, Spitzer, Gibbon, Williams, & Benjamin, 1997]; Beck Depression Inventory [BDI; Beck, 1996]; Beck Anxiety Inventory [BAI; Beck, 1990]) and provide simple clinical counts (e.g., number of rehospitalizations per year, number of suicide attempts, arrests, deaths, withdrawals from protocol). So far, results have been excellent, but the sample size includes subjects who were not in the current version of protocol, and it is small. Descriptions appear in Benjamin (2003, chap. 10) and Benjamin (in press). Later in this chapter, the IRT case formulation and treatment methods are explained and applied to Marianne. But first, there will be an exploration of the general diagnostic and conceptual problems that arise when trying to understand, develop, and validate effective treatment methods for people like Marianne whether from the perspective of IRT or any other approach.
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