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But do they That all depends on whether either firm (or both) is going to cheat on the cartel agreement. The best way to understand each firm s temptations is to first study the Prisoner s Dilemma, a game in which two criminal partners have to individually decide whether or not to cheat on an agreement they had previously made with each other to remain silent and not talk to the police about their illicit activities.

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Allendorf, F.W. & N. Ryman (2002) The role of genetics in population viability analysis. In: (eds Beissinger, S.R. & D.R. McCullough) Population Viability Analysis. University of Chicago Press, Chicago, 496 pp. Arino, O., M.L. Hbid & R.B. de la Parra (1998) A mathematical model of growth of population of sh in the larval stage: density-dependent effects. Mathematical Biosciences, 150, 1 20.

Columnar reports generally display one or more records per page, but they do so vertically. Columnar reports display data very much as a data-entry form does, but they re used strictly for viewing data and not for entering data. Figure 37-2 shows part of a columnar report (named Products) in Print Preview.

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Table 23.1 Effects of Divorce on Key Developmental Tasks of Intimacy and Identity and Therapeutic Goals Childhood Intimacy Developmental task Divorce danger Receiving love and attachment to parents. Intimacy/communion with parent jeopardized leading to anxiety and/or depression. Parental alienation. Intimacy/communion with stepparent(s) problematic. Developmental opportunity Cessation of conflict permits unrestricted bonding with parents. Experience love from another parental figure. More open kinship networks. Therapeutic goals Reassure continuing parental love and bond. Ensure cooperative co-parenting. Set realistic expectations for blended family relationships. Learning resilience. Identity

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In the longer term, expect to see the emergence of new video coding standards as processor performance continues to improve and previously impractical algorithms become feasible in commercial applications. Video coding research continues to be extremely active and there are a number of promising approaches that may nally replace the long-standing DPCM/DCT coding paradigm. These include (among others) model-based, mesh-based and wavelet-based video compression. It has been argued that the DPCM/DCT model has had 15 years of continuous development and is near its performance limit, whereas other techniques offer the possibility of greater potential performance (however, this argument has been circulating for a number of years now and DPCM/DCT is not yet dead!) It is possible that some of the more esoteric features of MPEG-4 Visual (e.g. object-based and mesh-based coding) may re-emerge in future standards alongside improved coding technology. It could be said that these tools are ahead of their time at present and may have more chance of commercial success if and when real application needs become apparent.

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We now show how to create a classi er with the AdaBoost machine. It follows the algorithm given in the work by Viola and Jones [27]. The machine is given images (x1 , y1 ), . . . , (xq , yq ) as input, where yi = 1 or 0 for positive and negative examples, respectively. In iteration t, the ith image is assigned the weight w(t, i), which corresponds to the importance of that image for a good classi cation. The initial weights are w(1, i) = 1/(2p), 1/(2n), for yi = 0 or 1, respectively, where n and p are the numbers of negatives and positives, respectively, q = p + n. That is, all positive images have 1 equal weight, totaling 2 , and similarly for all negative images. The algorithm will select, in step t, the tth feature f, its threshold value , and its direction of inequality s(s = 1 or 1). The classi cation function is h(x, f, s, ) = 1 (declared positive) if sf (x)<s , and 0 otherwise (declared negative). The expression |h(xi , f, s, ) yi | indicates whether or not h(x, f, s, ) correctly classi ed image xi . Its value is 0 for correct classi cation, and 1 for incorrect classi cation. The sum N w(t, i) |h(xi , f, s, ) yi | then represents the weighted i=1 misclassi cation error when using h(x, f, s, ) as the feature-based classi er. The goal is to minimize that sum when selecting the next WC.

(7.49) The generalized canonical ensemble (6. IS) for deriving the Boltzmann equation is thus given by

Figure 7.7 Constructing a graphical model based on the strengths of marginal dependences can lead to suboptimal results.

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