... 6.1 Contactless Cards 6.2 Reader Selection 6.3 PCB Design 6.4 Firmware Development 6.5 Evaluation Kit 7 Peripherals ...
Story - puput - 10/02/2010 - 07:55 - 0 comments - 0 attachments
Humans possess a menu of mating strategies that includes long-term mating, shortterm opportunistic copulations, extra-pair copulations, and serial mating (Buss, 1994/2003; 2007; Buss and Schmitt, 1993; Gangestad and Simpson, 2000; Greiling and Buss, 2000; Symons, 1979). Much empirical work has documented how mate preferences shift according to context. Women pursuing short-term mating compared to long-term mating, for example, increase the importance they place on a man’s physical attractiveness, sex appeal, muscularity, and extravagant and immediate resource displays (Buss and Schmitt, 1993; Frederick and Haselton, 2007; Gangestad, Garver-Apgar, and Simpson, 2007; Haselton and Gangestad, 2006; Haselton and Miller, 2006; Pawlowski and Jasienska, 2005). Women pursuing long-term mating, in contrast, place greater importance on resource acquisition potential, such as “has a promising career” and “has good financial prospects” (Buss and Schmitt, 1993).
The hardware technology that underlies computer systems has improved so rapidly and continuously for more than four decades that the ground rules for system design are constantly subject to change. It takes many years for knowledge and experience to be compiled, digested, and presented in the form of a book, so books about computer systems often seem dated or obsolete by the time they appear in print.
Even though some underlying principles are unchanging, the rapid obsolescence of details acts to discourage prospective book authors, and as a result some important ideas are never documented in books. For this reason, an essential part of the study of computer systems is found in current and, frequently, older technical papers, professional journal articles, research reports, and occasional, unpublished memoranda that circulate among active workers in the field.
Recent years see more and more importance of the management of credit risk for investors, especially institutional investors having large portfolio of corporate bonds, loans or other credit products. Questions like how to evaluate the credit value-at-risk given large amount of information (like different ratings and multiple credit metrics issued by different rating companies), how to build an efficient credit portfolio (having the highest expected return under certain level of credit risk) become increasingly difficult to solve using traditional methods and models. Especially for the second question, the rising dimension of the portfolio under limited computational speed call for leveraging some more robust algorithms for the large portfolio optimization.
In this paper, we will choose JP Morgan’s credit metrics model to evaluate the portfolio’s credit value-at-risk for the elaboration of our thesis and try to solve the problem of how to leverage multiple credit metrics (as a major input for the model) issued by different rating firms to largely reduce the negative impact of variation of different sources, for the slightest difference among the metrics might result in a huge deviation in the evaluation of the credit risk. At last we will introduce and exploit an increasingly popular and robust algorithm in today’s Large Scale Linear Planning Problem---Simulated Annealing to optimize our credit portfolio.
