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some of the books I d read had said that sometimes round numbers were good barriers for currencies. I had also read that once a resistance line (a price level above the current price) was broken, it would tend to become support, or a strong floor. This made sense to me. Once a currency pair did all that work to break above a level, some emotional and financial investment would be put toward keeping it above that level. Likewise, if the currency pair could break below a certain support level, then that level would tend to become strong resistance, or a ceiling. Once again, this immediately made sense to me. And it seemed to be happening on the chart in front of me. I noticed that three times in early March, the EUR/USD had tried to break below the 1.2200 level. Then on March 24, it finally broke through and closed below 1.2200. And what happened when it tried to rise back above that mark It couldn t do it. It was stopped cold at least two times that I could see on the chart. Those would have been nice times to sell. Perfect times, in fact. Just like now On March 30, it had broken above and below and shot upward. Then it had smashed through the line on April 2. Did that ruin my support/resistance theory Was the strength of the 1.2200 number diminished because of that (see Figure 16.2) It seemed to me that the line still retained some strength. A four-hour candle had broken the line. But what about the five-minute chart Maybe it looked like the four-hour candle had an easy time of breaking the level but it could have bounced on the shorter time frames.



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[1] L. Kleinrock, Queueing Systems, vol. I: Theory, Wiley-Interscience, 1975, pp. 171. [2] A.O. Allen, Probability, Statistics, and Queueing Theory with Computer Science Applications, 2nd edn. Academic Press, New York, 1990. [3] J.Y.B. Lee, On a Uni ed Architecture for Video-on-Demand Services, IEEE Transactions on Multimedia, vol. 4. no. 1. March 2002, pp. 38 47. [4] ComNets Class Library and Tools: http://www.comnets.rwth-aachen.de/doc/cncl.html





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Consequently, these users might nd them useful, although in the initial classi cation they are a priori considered as irrelevant (ie, these instances were not considered at all) Note that the Pareto diagram shown in Figure 43(b) enables the automatic discovery of the minimal subset of the sub-concepts, which covers the needs of most of the users For a formalization of this discovery process, including an evaluation study, we refer the interested reader to Stojanovic et al (2003b) The problem of post-pruning a hierarchy in order to increase its usability is explored in research related to modeling the user interface Previous work (Botafogo et al, 1992) showed the importance of a balanced hierarchy for the ef cient search through hierarchies of menus.

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This chapter investigates the issues in designing ef cient media servers for hybrid multicast streaming algorithms that integrate both closed-loop and open-loop algorithms. Existing media server designs are either optimized for closed-loop algorithms, which generate aperiodic data retrievals or optimized for open-loop algorithms, which generate periodic data retrievals. However, hybrid architectures such as the Super-Scalar Video-on-Demand (SSVoD) system in 19 require both periodic and aperiodic data retrievals which the existing server designs are sub-optimal. This chapter presents an ef cient server design to address this problem, which can achieve up to 60% capacity gains compared to conventional server designs.

I decided to switch down and look at a shorter time frame chart. It might sound lame to you, but when I saw the five-minute chart, chills ran up my spine (see Figure 16.3). The five-minute chart showed me exactly what I needed to see: that the 1.2200 level, overall, had retained its strength. Even though a four-hour

In 19 we presented a Super-Scalar Video-on-Demand (SS-VoD) architecture combining the batching, patching, and periodic broadcasting for implementing scalable and ef cient VoD services. In a SS-VoD system, multicast channels are divided into two types static channels and dynamic channels. Each channel transmits video data at the video playback rate using network multicast. Static channels are organized in a time-staggered manner to stream the whole video repeatedly and periodically. Dynamic channels are scheduled with batching and patching to enable clients to begin playback quickly. By simultaneously caching data from a static channel, the client can eventually merge back to an existing static channel and release the dynamic channel for reuse by other clients. In this chapter, we present an ef cient disk-array-based server design for implementing the video server in a SS-VoD system. The video server in a SS-VoD system is unique in that there are both statically scheduled and dynamically scheduled video channels. Existing video servers in general [1 5], and disk schedulers in particular [6 9], are designed either for systems with statically scheduled video channels (i.e., open-loop algorithms in 18), or for systems with dynamically scheduled video channels (i.e., closed-loop algorithms in 17). The

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