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Social-cognitive theory seems to have much potential in accounting for perceived effort and effort tolerance. Recent studies indicate that task-specific variables such as commitment /determination and the effort one is ready to invest in and tolerate while experiencing exertion account for substantial amounts of the variance in effort tolerance. Physical self-efficacy and perceived competence in tolerating effort and discomfort appear to be strong predictors of discomfort tolerance. However, no intervention studies have yet been conducted to examine this contention in an exercise setting. The relationship between self-efficacy and perceived effort and the impact of these perceptions on task persistence is a fruitful avenue for future research. Studies that go beyond the correlational nature of the efficacy-perceived effort relationship and directly manipulate self-efficacy are warranted. Such studies will advance our knowledge of how we might structure interventions to maximize efficacy, and in turn influence both psychosocial and behavioral outcomes associated with exercise (McAuley & Blissmer, 2000). As stated previously, few studies have attempted to examine the effects of goal orientations on perceived effort and effort tolerance. Findings from the limited number of studies available to date indicate that task orientation is associated with a superior level of coping with exertive experiences compared to ego goal orientation. However, further research is needed to confirm this assertion. New research directions pertaining to the influence of psychological factors in determining perceived effort and effort tolerance have adopted a multidimensional approach. Recently, Hutchinson and Tenenbaum (in pressb), and Ekkekakis et al. (2004) have demonstrated that different dimensions of effort are perceived distinctly during exercise and operate differently in the duration of an exertive task. Arent et al. (2004) observed similar trends postexercise. Together, these findings imply that feelings of effort are a consequence of several physiological and psychological determinants. To study the dependence of perceived effort on one physiological index is an oversimplification of the psychophysiological construct. Accordingly, a single-item measure of effort, such as Borg s (1998) RPE scale, is insufficient to capture the whole range of sensations that people experience when exercising or when being physically active (Hutchinson & Tenenbaum, in press-b). An adequate theory of effort perception ought to sufficiently account for the distinct inputs that shape the perceptual milieu during sustained physical activity. Future studies of perceived effort and effort tolerance using mul-
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Movable Type 3 provides a new package that should be the basis for any MT plugin that targets version 3 or later. The MT::Plugin package enables your plugin to identify itself formally with Movable Type. MT::Plugin descendants are visible in the MT web interface and can provide URLs for configuring the plugin as well. A typical plugin package utilizing MT::Plugin looks like what is shown in Listing 8-6.
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Exhaustive Search Optimization Algorithms in this class will rst form all possible query plans for a given query and then select the best plan for the query. Dynamic programming (DP) [Selinger79] (see Section 4.4.2.3) is an example of an algorithm in this class. Since the solution space containing all the possible query execution alternatives using DP is very large it has an exponential time-and-space complexity. Many different DP algorithms attempting to reduce the solution space and lower the time and/or complexity have been proposed [Graefe87] [Ono90] [Pellenkoft97]. We will review two such algorithms in Section 4.4.2.4. Heuristics-Based Optimization Originally, most commercial DBMSs used heuristicsbased approaches, which are also known as rule-based optimization (RBO) approaches, as their optimization technique. The algorithms in this class have a polynomial time-and-space complexity as compared to the exponential complexity of the exhaustive search-based algorithms, but the heuristics-based algorithms do not always generate the best query plan [Steinbrunn97]. In this approach, a small set of rules (heuristics) are used to order the operators in an RA expression without much regard to the database s statistics. One such heuristic is based on the observation that if we reduce the size of the input relations to a join, we reduce the overall cost of the operation. This rule is enforced by applying the select and/or project operations before the join. This rule is also known as pushing the select and project toward the leaves of the query tree. Another heuristic joins the two smallest tables together rst. This is based on the belief that since not all tuples from the relations being joined qualify the join condition, fewer tuples will be used in the next operation of the query tree, resulting in a smaller overall cost for the query. Another popular heuristic is simply to disallow/avoid using the cross-product operation, which is based on the fact that the intermediate result of this operation is usually a huge relation. These rules (and others like them) are used in many of today s commercial systems. In DB2, for example, there is a rule that forbids/avoids the use of an intermediate relation as the right operand of a join operation. In Oracle 10g, the rule-based optimizer (RBO) associates weights with each operation and uses the operation with the smallest weight rst. For example, Oracle associates a weight of 5 with a select operation that uses a unique index, while it assigns a weight of 20 to a relation scan. As a result, when given the choice, the RBO will use the index lookup to nd a single tuple rather than scanning the table. Oracle s RBO generates the query plan by applying this rule repeatedly to all operations in the query tree. One advantage of using a small set of rules like these is that the optimizer can generate a plan quickly. Although Oracle s RBO
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and full of potential downfalls. Sure, it s great to think about an ideal world where everyone is transparent, is driven by values, is inspired by common goals, treats each other well and fairly, and unites behind the common good; but that s just not the way it is. I would be insulting you if I did not acknowledge that we all carry a set of personal experiences that make it seem like some of the ideas I present throughout this book are an idealist s pipe dream of a world that will never be. But in the pages that follow, I hope to show you that the world that formed and informed most of these prior experiences the business-is-war, information-is-power, to-the-victorgo-the-spoils world of run-and-gun capitalism no longer exists. Advances in technology, communication, integration, and connectivity have converged with predictable cycles of history to create a sea change in the way we do business, and in the way we live our lives. Things have changed faster than we have developed new frameworks to understand them, and I hope to show you in great detail exactly how radical and permanent these changes are. To thrive in the hypertransparent, hyperconnected world of the twenty- rst century, we need to change, too. Throughout this book, I show you how qualities most people think of as soft trust, respect, transparency, purpose, reputation have become the hard currency of achievement in a connected world the drivers of ef ciency, productivity, and pro tability. You will come to understand that the HOWs of human conduct will be the determining factor in your long-term success. At rst blush, these ideas may seem to contradict much of what you believe or seem counterintuitive. By book s end, you might feel differently. Waves are fun; that is their greatest bene t. Standing up, waving your arms, screaming your head off for the home team, and, most important, being connected to everyone else in the stadium when you do so, that s fun. But Krazy George told me that the most signi cant thing about his rst Wave, and every Wave he has made since, is how it changes everything that comes after. For the rest of the game, the crowd cheers more vigorously. They are more excited and engaged in the outcome. They feel more a part of the experience. The Wave is not only powerful in itself; it unleashes long-term, enduring power in its wake. That is an essential property of power; once the circuit is complete, the current continues to ow.
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Alternatively, one may dispense with the normality assumption altogether and choose to work directly with the observed empirical distribution of total minutes for churners and nonchurners. We are interested in comparing p(T = 800) for each distribution; why not estimate this probability by estimating p(798 T 802) directly for each distribution It turns out that three of the churner customers had between 798 and 802 total minutes, compared to one for the nonchurner customers. So the probability estimates would be for the churners, 3 = 0.006211 p(T = 800|C) = 483 and for the nonchurners, 1 = 0.0003509 p(T = 800|C) 2850 Thus, to nd the naive Bayes classi cation, for churners, p(I V T = 800|C)P(C) = P(I |C)P(V |C) f T |C (800)P(C) = 0.2836(0.1656)(0.006211)(0.1449) = 0.00004227 and for nonchurners, p(I V T = 800|C)P(C) = P(I |C)P(V |C) f T |C (800)P(C) = 0.0653(0.2954)(0.0003509)(0.8551) = 0.000005788 [Here, f T |C (800) represents our empirical estimate of f T |C (800).] Thus, once again, the naive Bayes classi cation for new customers who have 800 total minutes and
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Psychologists do not engage in sexual intimacies with current therapy clients/patients.
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The data-processing inequality can be used to show that no clever manipulation of the data can improve the inferences that can be made from the data. De nition Random variables X, Y, Z are said to form a Markov chain in that order (denoted by X Y Z) if the conditional distribution of Z depends only on Y and is conditionally independent of X. Speci cally, X, Y , and Z form a Markov chain X Y Z if the joint probability mass function can be written as p(x, y, z) = p(x)p(y|x)p(z|y). Some simple consequences are as follows:
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List: Use this option to create a list. This option is similar to an array, but the list cannot be changed after an instance if the user-defined component is in your movie. The user can choose only one item from the list. String: Use this option to create a string literal object. Add this object to your component when you need the ability to frequently update text within a dynamic text box. Number: Choose this option when you need to create numeric data. You can use ActionScript to perform mathematical calculations on this type of data whereas if you enter numbers into a string object, Flash reads them as text objects. Boolean: Use this object type to define whether a particular object the user-defined component refers to is enabled (true) or disabled (false). Font type: Use this object type to refer to the font in a text object. Color: Choose this data type to define a text object s color. You can also use this object type if your component contains ActionScript that modifies and object s color with the setRGB method of the Color object.
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Part IV Working with UserForms
SUMMARY This analysis of I-O psychology s history demonstrates that the rise of the discipline during the past 100 years was the
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