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Creating the actual tabbed box functionality is easy enough, and there are a ton of scripts available out there if you don t want to do it yourself. The one in the following example is simple enough for most cases, but it would also work well with some bling. After all, if you ve decided to use a tabbed box, why not make it look really good and fit your design This is the code we ll be working with in this case. It relies on the Prototype JavaScript library, so you need to load that with wp_enqueue_script(). See the JavaScript section in 12 for more on that. In this example, assume that Prototype is loaded. Here is the part that needs to go in the head section in header.php:

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[1] D.A. Patterson, G. Gibson, and R.H. Katz, A Case for Redundant Arrays of Inexpensive Disks (RAID), Proc. of 1988 ACM SIGMOD Conference on Management of Data, Chicago, June 1988. [2] S. Ghandeharizadeh and D. Kim, On-line Reorganization of Data in Scalable Continuous Media Servers, Proceedings 7th International Conference on Database and Expert Systems Applications, Sept. 1996. [3] A. Goel, C. Shahabi, S.-Y. Yao, and R. Zimmerman, SCADDAR: An Ef cient Randomized Technique to Reorganize Continuous Media Blocks, Proc. International Conference on Data Engineering, 2002. [4] J.S. Plank, A Tutorial on Reed-Solomon Coding for Fault-Tolerance in RAID-like Systems, Software Practice and Experience, vol. 27, no. 9, Sept. 1997, pp. 995 1012. [5] L. Rizzo, Effective Erasure Codes for Reliable Computer Communication Protocols, ACM Computer Communication Review, vol. 27, no. 2, Apr. 1997, pp. 24 36. [6] A.J. McAuley, Reliable Broadband Communication Using a Burst Erasure Correcting Code, Proceedings of the ACM Symposium on Communications Architectures and Protocols, Sept. 1990, pp. 297 306.

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The parallel server architectures solved the scalability problem of the media streaming server by combining multiple low-cost media servers into one high-capacity streaming system. In very-large-scale systems such as providing video-on-demand services to millions of users, the resultant network traf c generated will be very substantial. For example, to stream a media of 4Mbps (e.g., high-quality video encoded in MPEG2 or MPEG4) to just 10,000 users, we will need an aggregate bandwidth of 40Gbps which only optical ber can carry in today s networks. Interestingly, as many studies have shown, most users will probably request a small portion of popular media contents, such as popular songs, movies, or documentaries. Thus, within the huge volume of network traf c there are in fact a great deal of duplicated data being transferred from the servers to the clients. Now if the data are the same, can we send a single copy instead of sending many duplicated copies to the clients This is the question being addressed in Part III of the book. In particular, we investigate the use of network multicast to deliver media data from the servers to the clients. Unlike the unicast model used in today s Internet, network multicast allows a sender to multicast a single packet to be received by multiple receivers. This has the potential to signi cantly reduce the network traf c in sending duplicated media data to a large number of users. In this chapter, we give an overview of network multicast in general, and IP multicast in particular, and discuss how media streaming can be carried over network multicast. In the subsequent chapters we will cover in more detail three classes of multicast streaming algorithms, namely, closed-loop algorithms, open-loop algorithms, and hybrid algorithms.

Today s Internet employs a form of data delivery known as unicast. In unicast a sender sends a data packet through the network which is eventually received by one and only one receiver, thus unicast is also known as point-to-point communications. This unicast data delivery model works well for a wide variety of applications, including the World-Wide-Web, email, le transfer, and even streaming continuous media such as audio and video.

The input pattern x feeds the v layer, the v layer feeds the z layer, and so on (Fig 6-1b) 6-5 Exercising a Neural-network Model

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