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To support the development a particular family of software systems or application frameworks [POSA1], a broader viewpoint should be applied to the set of available patterns. In particular, patterns should not be considered solely as islands. They should instead be woven into networks of interrelated patterns that define a process for resolving software development problems systematically [Cope96] [Gab96] [Cope97]. We call these pattern languages. Pattern languages are not formal languages, although they do provide a vocabulary for talking about particular problems [SFJ96]. Together, patterns and pattern languages help developers communicate architectural knowledge, learn a new design paradigm or architectural style, and elude traps and pitfalls that have been avoided traditionally only through costly experience [PLoPD1]. One or more patterns define the 'entry point' of a pattern language and address the coarsest-grained problems that must be resolved when developing a particular type of application or part of an application. When reading this book, you may identify the architectural patterns as those addressing coarse-grained problems in software architecture.

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Using this general methodology, we create a set of benchmarks for ve of the most commonly used statistical distributions: the normal distribution, the central t, F , 2 , and gamma These tests are archived at ICPSR7 as Publication Related Archive Dataset 1243, and are also available from the Web site that accompanies this book To our knowledge, this is the rst and only set of benchmarks available for this purpose In particular, we generate a set of p-values at regular and random intervals along { [1e-12,1-1e-12],0,1 } We use Kn sel s (1989) package u to generate reference values and checked these against identical computations using Brown et al s (1998) high-precision libraries We calculate correct values for a number of central inverse distribution for those p-values (to ve signi cant digits) and the corresponding p-values for those inverse values.

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Colcombet and Fradet (2000) present a general method for assuring that a program respects a safety property. First, they de ne a map from program entities, including function calls, to an abstract set of events. Next, they express the desired property as a nite state automaton over the alphabet of abstract events. Finally, they express the program as an abstract graph, whose nodes are program points and whose edges are instructions that generate events. Instead of executing the original program, they execute the product of the graph with the automaton. The resulting instrumented graph (I-graph) has the same behavior as the original program but only allows execution traces that satisfy the property speci ed by the automaton. By statically analyzing the I-graph, they minimize the number of inserted safety checks. They express the algorithms for minimization in terms of NP-complete problems and suggest heuristics for solving them. Unfortunately, they do not present any performance measurements, so it is not clear whether their approach is useful in practice. They also consider only properties captured by nite state automata, which cannot easily account for resource use.

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Where distributions required ancillary parameters, we generate these at regular intervals in [1e-4,1e6], constrained by the legal range for the particular parameter Our benchmark is meant to probe only some key aspects of the accuracy of a statistical package It is not comprehensive, and some applications may require accuracy in distributions that we do not test, or beyond the p (or other parameter range) that is covered by our procedure For example, Kn sel (1995, 1998, 2002) u and McCullough (1999a,b) clearly examine a number of areas we do not: the extreme tails t < 10 12 (inaccuracies are reported for some distributions at very extreme tails, eg, 10 70 ), a number of discrete distributions, and a number of noncentral distributions.

Stewart Living Omnimedia billed her company $17,000 per year for a weekend driver and other costs (New York Times, Feb 22, 2004) Another source of inaccuracy is in the computer calculationThe same question, asked two different ways to a computer can get two different answers 9 gave a glimpse into the logic used by computers We provided some tricks to getting more precise output, and verifying the results If numbers look too good to be true, get a second opinion Use a different software, rescale the data, or do a back-of-the-envelope double check At the end of the day, the computer is not going to refund your money for its calculation errors Once these four areas are con rmed, it is vital to incorporate downside risk into investment decisions Since we devoted a chapter to mathematical tools, it may be useful to add an example discussed by JA.

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Extracting text from PDFs in C# - Stack Overflow
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