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However, relatively little ongoing training is provided in this skill, and when training is offered, it is generally provided at a time when professionals have already established their interviewing styles (Ericcson and Charness, 1994; Fisher, 1995; Fisher and Geiselman, 1992) Second, if forensic interviews and assessments are not being conducted in the clients rst language, the linguistic constraints may preclude their ability to provide a free account that could be understood clearly Third, there may be a false belief among some professionals that persuasive questioning tactics are more effective than open questions in making guilty suspects confess To the contrary, the available research suggests that most suspects who confess do so at the beginning of the interview Those few who confess as a result of the interviewers tactics include a sizeable proportion of false confessors (Milne and Bull, 1999, p 89).

The reader (especially one who has not been paying close attention) may, at this point, ask, So what The end result is pretty much the same The answer is: The end result is not necessarily the same First, by filing a declaratory judgment action, the loathsome infringer is able to choose the battlefield (If this doesn t seem important, reread the section of this chapter entitled Jurisdiction and Venue ) Second, the disgusting scalawag is able to choose the timing of the action This may be significant for several reasons A patentee may, for example, have planned, indeed prepared, to take action against a different infringer (remember that infringement is often industry-wide) These plans and preparations may well be totally upset when the patentee is unexpectedly hauled into a different court to face a different infringer.

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6.10 FURTHER READINGS Several general introductions in data mining [13,14] provide excellent references for clustering algorithms. Basic reference books for clustering algorithms are authored by Jain and Dubes [6] and Kaufmann and Rousseeuw [7]. Recent surveys such as those by Berkhin [2] and Jain et al. [5] allow the reader to get familiar with current issues in clustering.

It is also possible that the patentee is engaged in some activity, such as an IPO, a merger, or an acquisition, that renders litigation especially burdensome at that time Finally, many self-proclaimed experts (euphemism for someone who has actually observed a patent infringement trial ) maintain that there is a psychological advantage that accrues to the plaintiff There may well be something to this theory Certainly, it is difficult for a patentee to convincingly present a courtroom image of righteous indignation when the only reason he or she is in court is that the (alleged) infringer dragged him there Standing, which is a prerequisite to the bringing of a declaratory judgment action, is, essentially, a requirement that the plaintiff (the pestilential infringer) have an objectively reasonable apprehension of imminent suit by the (righteous, oppressed) patentee.

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An express charge of infringement by the patentee is not required to create such an apprehension Rather, the courts look at the totality of the circumstances Briefly and bluntly put, almost any assertion concerning the scope of a patent will confer standing, whether such assertion is made directly to.

Last, the aforementioned organizational imperatives to provide interviews that follow a prede ned sequence are likely to represent a further obstacle to best practice..

If an exhaustive search is infeasible or very costly, a standard solution is to use some heuristic search method, for example, greedy search. With a greedy search method a graphical model is constructed from components, each of

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parallel, and loop structures for moment generating functions (MGFs) of the distributions of the waiting times of interest. In Section 2.5 we present a systematic procedure for solving larger owgraphs such as those of Figure 2.1. 2.1 FLOWGRAPH BASICS: MODELS FOR SERIES STRUCTURES In a owgraph model, the states or nodes represent outcomes. This is distinct from a graphical model where the nodes represent random variables. Flowgraphs are outcomes graphs, whereas graphical models are variables graphs. In the cancer example the nodes represent various stages of cancer or death. The nodes are connected by directed line segments called branches. Each branch has a transition probability and waiting time distribution associated with a transition from its beginning and ending nodes. The branches are labeled with transmittances, which involve probabilities and moment generating functions (MGFs). De nition 2.1.1. The moment generating function of a random variable T is de ned as M(s) = E(esT ), provided that the expectation exists for s in an open neighborhood of 0. Often, we write MT (s) to emphasize that it is the MGF of the random variable T , or equivalently, of the distribution of T . (2.1)

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