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the price of the Treasury bill one day after purchase will be quite close to the price of the previous day. But the point is that using words like close or likelihood simply underscores that we are ultimately talking about something that is not 100 percent certain. This particular uncertainty is called the uncertainty of price. Now let us add another layer of uncertainty regarding bonds. In a coupon-bearing security with two years to maturity, we will call our uncertainty the uncertainty of reinvestment, that is, the uncertainty of knowing the interest rate at which coupon cash flows will be reinvested. As Figure 2.2 shows, instead of having a Treasury security with just two cash flows, we now have six. As shown, there is a cash outlay at time of purchase, coupons paid at regular six-month intervals, and the receipt of par and a coupon payment at maturity; these cash flows can be valued with 100 percent certainty at the time of purchase, and we assume that this two-year security is held to maturity. But even though we know with certainty what the dollar amount of the intervening coupon cash flows will be, this is not enough to state at time of purchase what the overall total return will be with 100 percent certainty. To better understand why this is the case, let us look at some formulas. First, for our three-month Treasury bill, the annualized total return is calculated as follows if the Treasury bill is held to maturity: Cash out cash in Cash in 365 90

After determining a(t),7 PM again uses KMV s proprietary mapping from distance to default to EDF and determines the forward default probability HEDFt from the obtained forward distance to default (tDDH). The forward default probability HEDFt is now inserted in equation 4.26 to obtain the forward quasi-risk-neutral default probability, QDFi, and the RCV calculation at horizon can be completed using H equations 4.23 to 4.25.

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Abstract classes without any constructors can be exposed by using the no init instead, as seen in the example below:

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Manager component and the various monitoring components (Performance Monitor and Fault Monitor). From an implementation point of view, the Service Assurance component can be conceived as partially distributed. Therefore, we distinguish between its distributed components and its centralized ones: Distributed components: each node has its own instance of each of these components. Service Quality Manager, Performance Monitor, and Network Action Component are envisioned as distributed components. Centralized components: these components have a centralized nature, although there might be multiple instances of them over a large network. Assurance Manager, SLA Manager, and Fault Monitoring are thought of as centralized components, as they need information from different nodes and that causes a considerable complexity in decision making. For example, the SLA Manager will probably need performance measures from different nodes to decide whether a SLA has been violated or not. In order to communicate with different nodes for assurance purposes, the Service Quality Manager has external APIs in addition to the APIs offered to the internal assurance components on the same node. The external APIs allow remote nodes to access and process the monitoring information available at the local node. For example, assume that node A has two possible routes to reach node D. These routes are through node B and C. At any given point in time, node A may check the status of the interfaces of node B and C (congestion, failure, etc.) in order to decide which is the best route to reach node D and, on this basis, perhaps alter its routing table.

The OptIPuter project s two main visualization applications, JuxtaView and Vol-aTile, run on clusters of computers and stream the results to LambdaVision [3,5,6]. JuxtaView is a tool for viewing and interacting with time-varying large-scale 2D montages, such as images from confocal or electron microscopes or satellite and aerial photographs. Vol-a-Tile is a tool for viewing large time-varying 3D data, such as seismic volumes. JuxtaView and Vol-a-Tile are unique in that they attempt to anticipate how the user will interact with the data, and use the available network capacity to aggressively pre-fetch the needed data, thus reducing the overall latency when data is retrieved from distantly located storage systems. 2.2.3.4 Collaboration

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