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Instructions and material necessary for cloning cable modems and cable STBs are readily available over the Internet. It is highly probable that this capability will also emerge for IPTV STB smart cards. In addition, it is possible that smart card copiers will exist on the black market. Unauthorized duplication of the smart card could also be the result of an intruder able to perform management activities. Publicly available tools can be used to discover the existence of STBs and potentially their vendor and version numbers, which can be used to search for known vulnerabilities that can be exploited to provide access to the STB, thereby allowing unauthorized browsing of STB smart cards. Tampering with STB credentials could result in modification of their expiry to indicate they have expired, resulting in interruption of the IPTV service. Deletion of the STB credentials prevents the STB from authenticating with the IPTV service, resulting in interruption of the IPTV service.
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As we saw in the last chapter, marketing s role has not changed. It s still about de ning target markets, communicating with prospective customers, building loyalty, and so on. But the techniques that were successful in the past will be less and less effective in the future. This is where your new marketing mindset comes in. Clear your mind of all those one-way, one-sided communication techniques, all those ways of spouting only your side of the story. Marketing to the social web is not about you getting your story out; it s about your customers. It s about being more transparent, earning trust, and building credibility. It s about nurturing relationships and dialogue among customers, prospects, your company, and whoever else is active in the community. To win the branding war, you have to recognize that brand equity is shifting away from brand essence and brand recall. Those were key elements in the old marketing and resulted in what I call the stationary brands, like Gillette, Kodak, Disney, and Kellogg s. But a brand is actually a living, changing thing, especially in the new marketing. It is dif cult for many marketing people and C-level executives to admit that their brand is a living thing. Yet, in the new marketing reality, the brand is based on the dialogue you have with your customers and prospects the stronger the dialogue, the stronger the brand; the weaker the dialogue, the weaker the brand. What makes the social web so important is that it permits companies to have these kinds of dialogues more ef ciently and less expensively than ever in the past. Google is a good example of a living brand that fosters dialogue. Its various products enable dialogue between users in so many ways features like Google Talk, Google Mobile, Google Groups, Blogger, YouTube, and Picasa. The brand has become iconic because it s an indispensable part of everyday life for anyone who uses a computer (or a cell phone). Google is always
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it is possible that Black would have never become involved in option pricing. Fired up by Treynor s mentorship, Black caught nancial economics fever. He began to mingle with leading academics, and in mid-1967 initiated his lifelong tradition of penning working papers dealing with nancial risk, corporate investments, and assorted nance-related stuff. These initial ruminations were dominated by CAPM in an almost obsessive fashion. A CAPM world is a world where equilibrium reigns supreme and markets are ef cient. This ideal appealed so much to Black that he decided to devote his existence to making it come true. The future father of modern option theory staunchly believed that the future belonged to CAPM. It is thus not surprising that back in those early days Black would reach the conclusion that CAPM could be applied to solving just about any nancial problem, including, yes, option pricing. That particular quest received an extra push when Black enlisted a partner-in-crime. Shortly after arriving at the Massachusetts Institute of Technology s Sloan School of Business in September 1968, rooky nance professor Myron Scholes telephoned Black at next-door ADL and met him for lunch. Having heard about Black s nance work through a fellow PhD student at the University of Chicago (soon-to-be star academic Michael Jensen), Scholes thought that here was someone that he could collaborate with, a devoted fan of the new nance theories with an unrestrained interest in applying them to real-life problems. So eager was Black to embark on the theoretical crusade that he quit his ADL job in early 1969 and set up a consultancy deemed Associates in Finance, with Scholes as reliable contributor. His rst assignment consisted in proposing a CAPM-compliant investment strategy to Wells Fargo Bank, which was not eventually adopted. Not to be discouraged, Black and Scholes continued to toil together along similar lines. Soon, they found an extra reason to pick each other s brains. It is not entirely clear why Fischer Black began to be interested in options, but it may all have boiled down to his mission to apply CAPM to all kinds of assets beyond the model s original focus on stocks. Whatever the initial inspiration, by June 1969 he had distilled the challenge of valuing a stock warrant to a partial differential equation (a complex, dif cult-to-solve, mathematical expression). He was unfortunately unable to solve it by himself, so he put the whole thing aside. Nonetheless,
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knees That the crisis would not have happened had we never embraced theoretical utensils Well, the point of genesis took place in the decidedly unsophisticated, absolutely non-ivy-league world of the shaky, door-todoor, subprime American home loan (down) market. No PhDs there. No Nobel Prizes. No stochastic calculus. The math began to play a role once those dubious mortgages began to be packaged and traded as part of latest-generation derivatives toys. Once the game moved over to the trading oor side of the fence, the math began to have a serious impact as friendly enabler. Valuation models (most notably of the Gaussian Copula type) decisively contributed to enticing punters, by delivering the right credit ratings (senior tranches) and the right prospective returns (equity tranches). Risk measurement techniques (VaR) propagated the comforting idea that everything was calmly safe for credit transacting to proceed unfettered. Capital-charge-determining analytical tools (VaR again) gave permission for the buildup of you-only-live-once leverage. If you wanted to play the structured mortgage-linked credit derivatives attraction in earnest, you had a loyal friend in quantitative nance and those who peddle its virtues. It is thus possibly non-incoherent to argue that theory contributed massively to the crisis by contributing extraordinarily to the nuclear-racestyle amalgamation of toxic securities on banks balance sheets. After all, bank losses are what the crisis is truly known for. The key question is, was the quant stuff an absolute requirement for such wild-eyed behavior Was the math the only conduit through which the craziness could have been enacted Not really. After all, Moody s and Standards & Poor s could have assigned AAA nomenclatures using some other kind of alibi (maybe a Subprime is Sublime tagline), market valuations could have reached similar levels through rough-and-tumble, and regulators may have allowed similarly generous capital terms via simple man-to-man handshakes. The net effects would not have been abominably indistinct. Unlike in the case of portfolio insurance where abidance by a very precise mathematical recipe de ned the thing in itself (note that the same argument would be valid for the delta-revering strategies that led to the 2005 correlation meltdown), in the case of the credit meltdown that rst reared its ugly head in mid-2007 there were several other plausible, nontechy, paths towards resembling outcomes. Having said that, the main point remains that the quant stuff was unalterably responsible. It is true that by deciding to endow the
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Classi cation model. A model where the response variable is categorical. Classi cation tree. A decision tree that is used for prediction of categorical data. Cleaning (data). Data cleaning refers to the detecting and correcting of errors in the data preparation step. Cleansing. See cleaning. Clustering. Clustering attempts to identify groups of observations with similar characteristics. Complete linkage. Maximum distance between an observation in one cluster and an observation in another one. Concordance. Re ects the agreement between the predicted and the actual response. Con dence interval. An interval used to estimate a population parameter. Con dence level. A probability value that a con dence interval contains the population parameter. Constant. A column of data where all values are the same. Consumer. A consumer is de ned in this context as one or more individuals who will make use of the analysis results. Contingency table. A table of counts for two categorical variables. Continuous variable. A continuous variable can take any real number within a range. Correlation coef cient (r). A measure to determine how closely a scatterplot of two continuous variables falls on a straight line. Cross validation. A method for assessing the accuracy of a regression or classi cation model. A data set is divided up into a series of test and training sets, and a model is built with each of the training set and is tested with the separate test set. Customer Relationship Management (CRM). A database system containing information on interactions with customers. Data. Numeric information or facts collected through surveys or polls, measurements or observations that need to be effectively organized for decision making. Data analysis. Refers to the process of organizing, summarizing and visualizing data in order to draw conclusions and make decisions. Data matrix. See data table. Data mining. Refers to the process of identifying nontrivial facts, patterns and relationships from large databases. The databases have often been put together for a different purpose from the data mining exercise. Data preparation. Refers to the process of characterizing, cleaning, transforming, and subsetting data prior to any analysis. Data table. A table of data where the rows represent observations and the columns represent variables. Data visualization. Refers to the presentation of information graphically in order to quickly identify key facts, trends, and relationships in the data. Data warehouse. Central repository holding cleaned and transformed information needed by an organization to make decisions, usually extracted from an operational database. Decimal scaling. Normalization process where the data is transformed by moving the decimal place. Decision tree. A representation of a hierarchical set of rules that lead to sets of observations based on the class or value of the response variable.
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Mass spectroscopy is a most promising areas for molecular mass determination of inorganic polymers in the twenty- rst century. Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) provides a soft-ionization technique for mass measurements. Soft-ionization methods provide more parent ions than the usual mass spectrometry techniques that provide fragments of the molecule, with or without some of the parent ion. A polymer sample is dissolved or dispersed in an organic compound of low molecular mass that absorbs strongly at a wavelength accessible with an ultraviolet laser. Sodium or potassium metal ions are also a part of the matrix. The laser energy is transferred from the organic compound to the polymer molecules, which vaporize (desorb), taking with them one or more metal ions. Fortunately, the species with only one cation are more volatile and dominate the resulting mass spectrum, usually with a time-of- ight instrument. Individual molecular masses can be observed, even for organic polymers that have low molecular masses, if the number-average molecular mass is less than 10,000, and for inorganic polymers of high monomer molecular mass this limit should be much larger. Even when individual polymer masses cannot be discerned, the shape of the peak can be used to estimate both numberaverage and weight-average molecular masses (15). Other soft-ionization mass spectroscopy methods include electrospray ionization (16), eld desorption (17), and laser desorption (without added matrix) (18).
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In the preceding example, try.jar is the name of the JAR file and tagexample is the name of the package that will contain the class files. To confirm the packaging of the files in the JAR file, at the command prompt, type:
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