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hand, there are initiatives extracting the information in a content-driven (bottom up) manner. Goal-driven systems, for example, SMES (Declerck and Neumann, 2000), are generally domain speci c and make use of knowledge about the domain in question. The information of interest typically is prede ned as open slots, in so-called templates, which are to be lled by the relevant parts of the text. Goal-driven systems often make use of shallow parsing techniques, and this has led to a renaissance of nite state techniques, for example, augmented transition networks or nite state transducers. Content-driven systems, on the other hand, are designed to work more or less domain-independently. Typically, content-driven systems build up ontologies/taxonomies based on heuristics as well as statistical techniques. Goal-driven and content-driven systems have in common that both of them are highly dependent on a thorough low-level linguistic analysis of a given text, comprising normalization, tokenization and part-of-speech (POS) tagging. As of today, many goal-driven systems perform reasonably well within their domains (for evaluation methods, see, e.g. the MUC conferences). However, their dependency on prede ned domains is a clear drawback and makes them rather unattractive in the Semantic Web context, or for agents or other domain-independent applications. This drawback has led to initiatives that are either trying to make goal-driven systems more domain-adaptive (e.g. SMES; Declerck and Neumann, 2000), or combining them with content driven techniques (e.g. GETESS; Dusterhoft et al., 1999). On the other hand, the domain-independence of content-driven systems comes with a de ciency in terms of performance. The lack of accuracy makes content-driven systems somewhat inappropriate as tools for extracting information units that require a high degree of precision (e.g. knowledge base generation). However, their fuzziness is an important feature when it comes to tasks dealing with uncategorized texts such as summarization or agent-based searching.

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Gate Voltage Vg (V) Figure 6.25 Hot-carrier degradation as a function of gate voltage. The different curve represent the different gate oxide splits. The nitrided oxides (NO) and reoxidized nitrided oxide (ONO) greatly reduce the peak of the hot-carrier damage. (After Hori et al., Ref. 120, 1992 IEEE.) metal pad. The pad, covered with resist, is in contact with the unprotected deposited metal until the exposed metal has been completely etched. No charge builds up on the pad until this point. Once the unprotected metal is completely removed, the charge begins to build up on the exposed metal sides at the edge of the resist mask. 94 The longer the overetch, the greater the buildup of charge. 92 The damage itself takes the form of interface states, slow interface states, 9 8 and positive hole traps. 92 This damage appears not be annealed out-interface states reappear after an alloy anneal, 99 and the hole traps remain, even after anneals as high as 9000C.100 Plasma damage has a significant effect on the hot-carrier hardness of the oxide.99 ,101 This added susceptibility comes from the prestressing of the gate oxide by Fowler-Nordheim injection that arises during the plasma processing. Initial measurements appeared to show that n-MOS transistors were affected by antenna ratios. However, later work99 showed that the n-MOS was much less susceptible to interface state generation but that it had increased electron trapping (at Vg = Vd). More striking was the decrease in p-MOS hot-carrier susceptibility. 98 The p-channel device shows a very marked increase in damage under electron trapping conditions (Vg just above threshold). In summary, the damage in both n- and p-MOS under hot-carrier stress for unprotected device with large antenna ratios takes the form of increased electron trapping susceptibility.

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T = 50 C 6000 Count (adu)

Stephen Jay Gould, naturalist, 1941 2002

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As mentioned earlier in the Ease of Access section, Windows Vista now makes it possible to use speech recognition software to work with your computer using your voice. Using commonly used applications, you can dictate documents or e-mails or even fill out forms. Speech recognition is part of the Section 508 legislation in the United States that required

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Property Recovery Model Level* D Graphic Control Management Studio Code Option ALTER DATABASE <DB Name> SET RECOVERY {FULL | BULK_LOGGED | SIMPLE} ALTER DATABASE <DB Name> SET TORN_PAGE_DETECTION {ON | OFF} EXEC sp_configure media retention EXEC sp_configure recovery interval

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This factorization is not unique; but it is advantageous to choose F (z 1 ) in such a way that all roots are within the unit circle. In such a case, 1/F (z 1 ) is a stable, realizable lter. Now, if the matched lter is concatenated with 1/F (z 1 ), then the noise at the output of this concatenation is white again, with an ACF given by N0 k . It is therefore also known as the noisewhitening lter. The sample values of the impulse response of concatenation of the basis pulse, wireless channel, matched lter, and noise-whitening lter is henceforth denoted as the discrete time channel . The impulse response of the discrete time channel is written as fk and its z-transform as F (z). Note that the impulse response of this channel is causal, so that the output signal can be written as

The Applications section shows the application Firefox will launch when it detects various multimedia file types in web pages. For each multimedia file type, you can select from several options:

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