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10 Writing Group of the International Menopause Society Executive Committee Guidelines for the hormone treatment of women in the menopausal transition and beyond Climacteric 2004;7:8 11 11 The North American Menopause Society Position statement Recommendations for estrogen and progestogen use in peri and postmenopausal women: October 2004 Menopause 2004;11(6):589 600 12 Skouby SO and the EMAS Writing Group (David Barlow, Martina D ren, G ran Samsioe, Anne Gompel, Amos Pines, Farook Al-Azzawi, Alessandra Graziottin, Decebal Hudita, Erdogan Ert ngealp, Joaquin CalafAlsina, Serge Rozenberg and John C Stevenson) Climacteric medicine: European Menopause and Andropause Society (EMAS) Position statements on postmenopausal hormonal therapy Maturitas 2004; 48:19 25 13 Sturdee DW, MacLennan A HT or HRT, that is the question (editorial) Climacteric 2003;6:1 14 Burger H, and the IMS Writing group Practical recommendations for hormone replacement therapy in the peri and post menopause Climacteric 2004;7:1 7 15 Chlebowsky RT, Hendrix SL, Langer RD, et al for the Women s Health Initiative Investigators In uence of estrogens plus progestin on breast cancer and mammography in healthy postmenopausal women: the Women s Health Initiative randomized trial JAMA 2003;289:3243 3253 16 Manson JE, Hsia J, Johnson KC, et al for the Women s Health Initiative Investigators Estrogens plus progestin and the risk of coronary heart disease N Engl J Med 2003;349:523 534 17 Million Women Study Collaborators Breast cancer and hormone replacement study Lancet 2003;362:419 427 18 Naftolin F, Schneider HPG, Sturdee DW, and The Writing Group of the IMS Executive CommitteeGuidelines for the hormone treatment of women in the menopausal transition and beyond: Position statement by he Executive Committee of the International Menopause Society Maturitas 2004;48:27 31 19 Genazzani AR, Gambacciani M, Schneider HPG, Christiansen C Controversial issues in climacteric medicine IV Postmenopausal osteoporosis: therapeutic options Climacteric 2005;8:99 109 20 Genazzani AR, Gambacciani M, Simoncini, Schneider HPG Controversial issues in climacteric medicine Series 3rd Pisa workshops HRT in Climacteric and aging brain Maturitas 2003;46:7 26 21 Alexander JL, Kotz K, Dennerstein L, Kutner SJ, Whallen K, Notelovitz M The effects of menopausal.

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can essentially ignore it The computer is much more likely to break Your job is secure Instead of using a shuffling technique, you can achieve the same result by choosing the pivot randomly instead of deterministically Take a random item in the array and swap it with the item in position l o w Take another random item and swap it with the item in position high Take a third random item and swap it with the item in the middle position Then continue s usual As before, degenerate partitions are always possible, but they now happen as a result of bad random numbers, not bad inputs Let us look at the differences between randomized and nonrandomized algorithms So far we have concentrated on nonrandomized algorithms When calculating their average running times, we assume that all inputs are equally likely This assumption does not hold, however, because nearly sorted input, for instance, occurs much more often than is statistically expected This situation can cause problems for some algorithms, such as quicksort But when we use a randomized algorithm, the particular input is no longer important The random numbers are important, and we get an expected running time, in which we average over all possible random numbers for any particular input Using quickselect with random pivots (or a shuffle preprocessing step) gives an O(N) expected time algorithm That is, for any input, including already sorted input, the running time is expected to be O(N), based on the statistics of random numbers On the one hand an expected time bound is somewhat stronger than an average-case time bound because the assumptions used to generate it are weaker (random numbers versus random input) but it is weaker than the corresponding worst-case time bound On the other hand, in many instances solutions that have good worst-case bounds frequently have extra overhead built in to assure that the worst case does not occur The O(N)worst-case algorithm for selection, for example, is a marvelous theoretical result but is not practical Randomized algorithms come in two basic forms The first, as already shown, always gives a correct answer but it could take a long time, depending on the luck of the random numbers The second type is what we examine in the remainder of this chapter Some randomized algorithms work in a fixed amount of time but randomly make mistakes (presumably with low probability) called false positives or false negatives This technique is commonly accepted in medicine False positives and false negatives for most tests are actually fairly common, and some tests have surprisingly high error rates Furthermore, for some tests the errors depend on the individual, not random numbers, so repeating the test is certain to produce another false result In randomized algorithms we can rerun the test on the same input using different random numbers If we run a randomized algorithm 10 times and get 10 positives-and if a single false positive is an unlikely occurrence.

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Topic 59: Income Tax Fundamentals and Calculations - 181 (1) The penalty equals 20 percent of any underpayment of tax attributable to the taxpayer s failure to make a reasonable attempt to comply with the law or intentional disregard of the law (2) The IRS has the burden of production to show that the taxpayer was negligent rather than he or she made an honest mistake This means the IRS must show a preponderance of evidence that negligence occurred D Civil fraud (1) The penalty is 15 percent for each month a return is late, up to a maximum of 75 percent Fraud is de ned as an intentional act of trying to cheat the government (2) The burden of proof for establishing fraud falls on the IRS The IRS must show clear and convincing evidence that fraud actually occurred E Criminal fraud (1) Also known as tax evasion, this it is a felony offense, punishable by severe monetary nes (up to $100,000 for an individual and $500,000 for a corporation) and by imprisonment in a federal jail (2) There must be guilt beyond a reasonable doubt for a person to be convicted F Tax return preparer penalties (1) An income tax preparer is any person who prepares tax returns (or employs other people to prepare returns) for compensation (2) If the preparer understates the amount of tax, and he or she knows (or should know) that the position has no erit, the IRS can impose a $250 penalty If there is a willful attempt to understate taxes, the ne increases to $1,000. 13 Generator In .NET Framework Using Barcode drawer for .NET Control to generate, create European Article .Related: 

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