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After you create your type on a path, you can go back and change the shape of the path later, and the type reflows along the new shape. The important thing to remember is that, when you create your type layer, Photoshop creates a new path based on the original path. This special path is called a type path; you can see it by selecting the type layer then displaying the Paths palette. Type paths are automatically created, and the only way to delete a type path is to delete the corresponding type layer. What this means is that, if you want to alter the shape of your type later, you need to edit this type path, not your original path. To select the type path for editing, select the type layer, display the Paths palette, and click the type path in the palette to select it. (You can also select the type path automatically by reediting the text in the type layer.) After you select the type path, you can use the pen tools and the other path tools such as the Direct Selection tool to reshape it just like any other path. The text automatically flows along the new shape of the path, as shown in Figure 4.29.

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Next is the final phase of the installation process. With VPN Client code version 6.01 and later, you are no longer required to reboot your PC for the application to work. You can optionally reboot, but it is no longer a requirement. The only button that is available to you during this phase is the Finish button. Clicking Finish returns you to Windows. You are now ready to use your VPN client. Figure 3-53 shows an example of this window.

When you create a new type layer, Photoshop uses the current foreground color as the default color for the type.

Professional Photographers of America (PPA)

Figure 34-10

size and shape of a neuron The net result is a very restricted range of frequencies within a pulse burst with no way to create pulse code modulation The length of a standard neural burst of the type most often published depends on a natural rhythm associated with short-term memory Usually, there are about 10 pulses with a characteristic spacing as shown later in the chapter Random isolated spikes are possible, and their purposes, if any, are poorly understood, but they seem to be very much in the minority of neural actions When many neurons re at the same time, this can be interpreted to be not a code, but rather, an indication that the brain is working normally Thus, we arrive at the simplest of codes: One action potential equals one bit of information, as assumed in 3.

The Version Number pattern guarantees that use cases executed across transactions will be properly isolated from each other s changes, in the same way that use cases that execute within a single transaction are guaranteed to be isolated from the operations of other transactions. However, what happens in the infrequent event that both moderators attempt to update the server (Steps 5 and 6) at the exact same time In this example, two instances of the Message entity bean could be loaded into memory with both containing the same version number. The call to checkAndUpdateVersion will thus succeed in both instances. Once the first transaction commits, the question then becomes: what happens when the second transaction attempts to commit The answer is that the second transaction will be correctly rolled back. Since both transactions are happening at the same time, the same transaction isolation level semantics that protect use cases that execute within one transaction will protect this particular operation from conflicts. The way it achieves this depends on how your database/application server handles concurrency:

The starting point is that those entities that have an interest in a project must be identi ed and the nature of their interests analyzed. Stakeholders can be identi ed, categorized, and prioritized by using several techniques including interviews with experts, brainstorming with project team members, and the use of checklists (Karlsen, 2002). A common mistake is committed by thinking that stakeholders are only those individuals who are decision-makers

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The model of a biological neuron developed in this book uses no analog weighting factors, no negative weighting factors, only Boolean values, and no summations, linear or otherwise It is a simpler sort of digital model that serves adequately for biological learning Neurons with Combinational Abilities Combinational learning is de ned below to involve the growth of new neurons and synapses that are dedicated to detecting new features, and doing so without pondering in the short-term memory system Dedicated feature-detecting neurons, the neurons represented by the encoder box of 4, are activated only when they detect a given feature, such as a shape, shade, or color Assumed to be the result of learning over many decades, featuredetecting neurons do only one thing in this model: Each is dedicated to detecting a given feature When they do so, they send a signal into a particular location in conscious short-term memory.