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5. N. Day and J. Joyce. The semantics of statecharts in HOL. In J. Joyce and C.-J. Seger, eds., Higher Order Logic Theorem Proving and Its Applications, Vancouver, British Columbia, Canada, 1994. Lecture Notes in Computer Science, vol. 780, pp. 338 351. SpringerVerlag, New York, 1994. 6. D. Harel. Statecharts: a visual formalism for complex systems. Science of Computer Programming, 8:231 274, 1987. 7. D. Harel and A. Naamad. The STATEMATE semantics of statecharts. ACM Transactions on Software Engineering and Methodology, 5(5):293 333, 1996. 8. A. Knapp, S. Merz, and C. Rauh. Model checking timed UML state machines and collaborations. In W. Damm and E.-R. Olderog, eds., Formal Techniques in Real-Time and Fault-Tolerant Systems, Oldenburg, Germany, 2002. Lecture Notes in Computer Science, vol. 2469, pp. 395 416. Springer-Verlag, New York, 2002. 9. R. Laleau and A. Mammar. An overview of a method and its support tool for generating B speci cations from UML notations. In 15th IEEE International Conference on Automated Software Engineering (ASE 2000), Grenoble, France. IEEE Computer Society Press, Los Alamitos, CA, 2000. 10. K. Lano, K. Androutsopoulos, and P. Kan. Structuring reactive systems in B AMN. In 3rd IEEE International Conference on Formal Engineering Methods, York, UK. IEEE Computer Society Press, Los Alamitos, CA, 2000. 11. K. Lano and D. Clark. Direct semantics of extended state machines. Journal of Object Technology, 6(9):35 51, 2007. 12. D. Latella, I. Majzik, and M. Massink. Automatic veri cation of a behavioural subset of UML statechart diagrams using the SPIN model-checker. Formal Aspects of Computing, 11(6):637 664, 1999. 13. H. Ledang and J. Souqui res. Contributions for modelling UML state-charts in B. In Integrated Formal Methods, Third International Conference, IFM 2002, Turku, Finland, May 2002. Lecture Notes in Computer Science, vol. 2335, pp. 109 127. Springer-Verlag, New York, 2002. 14. J. Lilius and I. P. Paltor. Formalising UML state machines for model checking. In R. France and B. Rumpe, eds., UML 99: The Uni ed Modeling Language Beyond the Standard, Fort Collins, CO, 1999. Lecture Notes in Computer Science, vol. 1723, pp. 430 445. Springer-Verlag, New York, 1999. 15. E. Meyer and J. Souqui res. A systematic approach to transform OMT diagrams to a B speci cation. In J. Wing, J. Woodcock, and J. Davies, eds., World Congress on Formal Methods in the Development of Computing Systems, FM 99, Toulouse, France, Sept. 1999. Lecture Notes in Computer Science, vol. 1708, pp. 875 895. Springer-Verlag, New York, 1999. 16. E. Mikk, Y. Lakhnech, M. Siegel, and G. J. Holzmann. Implementing statecharts in Promela/Spin. In Second IEEE Workshop on Industrial-Strength Formal Speci cation Techniques, Boca Raton, FL, 1998. IEEE Computer Society Press, Los Alamitos, CA, 1998. 17. H. P. Nguyen. D rivation de sp ci cations formelles B partir de sp ci cations semiformelles. Doctoral dissertation, INIST-CNRS, 1998. 18. OMG. Uni ed modeling language, superstructure, v2.1.2. http://www.omg.org/spec/ UML/2.1.2/Superstructure/PDF, 2007.
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Rule 5: Put the Power of Technology to Work
As discussed above, psychiatric disorders such as depression or anxiety disorder predict response to serotonin drugs. There is evidence that post-traumatic stress disorder responds to these drugs, and there are patients whose alcohol dependence is linked to this disorder. An argument has been put forward that impulsive, socially disorganized, early onset patients ( Type B or Type I , see Epstein, 2001) would be those who would respond to serotonin-enhancing drugs, but there has been little clinical support so far. If anything, the opposite is emerging. Kranzler et al. (1996) found type B alcoholics drank more when taking uoxetine than placebo, and Pettinati et al. (2000) found that the SSRI sertraline slightly improved the outcome for Type A alcoholics (later onset, more socially stable, less severe) but did not do so in Type B.
Most individual U.S. stocks bounce up and down more than the overall U.S. stock market. We can ease that roller-coaster ride reduce that volatility by adding more U.S. stocks, especially ones in different industries that march to a somewhat different drummer. We can strive to eliminate this diversi able risk. But after a point, we will still be left with the systematic risk of the overall U.S. stock market. Through statistical methods known as regressions, we can divide the volatility of each U.S. stock into portions that are: 1. 2. 3. 4. 5.
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