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Governmental entities that typically use these transactions include pension funds, state investment boards and treasurers, and college and university endowment funds Governments that enter into securities lending transactions are usually long-term investors; a high rate of portfolio turnover would preclude the loaning of securities because the loan might extend for a period beyond the intended holding period At the same time, securities lending transactions are generally used by governmental entities that are holders of large investment portfolios There are several reasons for this, such as the degree of investment sophistication needed to authorize and monitor these types of transactions, as well as the existence of enough critical mass of investments available to lend to allow the governmental entity lender to earn enough profit on these transactions to have an acceptable increase in the overall performance on the investment portfolio.

Let us now try a different approach to the same proble m, Making use of Tab le 13,2, let liS take the Laplace tra nsform of both sides of Eq, ( 14, I),

In addition, many lending agents are not interested in being involved with securities lending transactions for smaller portfolios GASBS 28 requires the following basic accounting treatment for securities lending transactions: The securities that have been lent (the underlying securities) should continue to be reported in the balance sheet Collateral received by a government as a result of securities lending transactions should be reported as an asset in.

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Referring to Eqn (386), we see that an kexp(2pinx/X )j f (x)l The requirement that f(x) be square integrable ensures that an is well de ned and nite The requirement means that the vector norm k f k k fj f l is well de ned A vector space with a norm suitably measuring distance between vectors is called a Hilbert space The space of square integrable periodic functions using the inner product of Eqn (389) is a Hilbert space Many families of basis functions have been developed to represent continuous functions In addition to the harmonic functions, the Hermite Gaussian functions mentioned in previous sections have been particularly popular in optics Families of special functions, such as Legendre polynomials and spherical harmonics, are popular in representing distributions in electromagnetic and quantum theory.

Overview of concept maps Concept mapping gives a good standardized framework for creating mental model representations, but identi es only relationship strength and not the nature of the relationship between concepts; participants are left to their own devices to determine how highly related concepts may interact with each other. Though binary symmetric similarity matrices might be integrated into text analysis tools for extracting information from interview transcripts, the data collected with concept mapping is typically aggregated for all analyses. By comparison, RCID allows for a more suitable parsing of analyses and permits multiple permutations of participant-group assignments without the need for gathering additional information.

This book is an introduction to some of the important techniques of text mining. Many of these are from other research areas such as information retrieval and statistics, and references for these topics are given at the end of the respective chapters. This section lists a few books that focus on text mining itself. Text mining grew out of data mining. A good introductory book on applying data mining to online texts is Data Mining the Web by Zdravko Markov and Daniel Larose [77]. Another introductory book on using Per1 to interact with the Web is Spidering Hacks by Kevin Hemenway and Tara Calishain [53]. For an introduction to text mining, try Text Mining by Sholom Weiss, Nitin Indurkhya, Tong Zhang, and Fred Damerau [ 1251. Their emphasis is on creating quantitative summaries of one or more texts, then applying data mining techniques. Finally, a more advanced book is The Text Mining Handbook by Ronen Feldman and James Sanger [43].

Since the mid-1980s, the generation of functional bases has become an advanced science in its own right in the context of wavelet theory We consider wavelet representations in Section 38 This section describes a startling approach to discrete representation developed by Shannon [219] in 1949 Under the Whittaker Shannon sampling theorem, a continuous function is represented on a basis such that the representation coef cients are equal to periodically spaced values of the function itself The core idea of the sampling theorem is that a signal may be fully represented by capturing a discrete sequence of signal values Detailed consideration of the relationship between sampling in measurement, analysis, and display is critical to understanding optical sensors It is not possible in practice to actually measure the eld at speci c points in space or time (Real detectors integrate eld values over nite space time windows.

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