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Mac OS X is based on Unix, which means there are zillions of processes running around your machine at any one time. Some of these processes are obvious: The big GUI applications like the Finder and iTunes are processes. But lots of other faceless processes are going on all the time. Applications sometimes spin off other processes to perform specific tasks, and the system itself runs invisible processes to do various jobs. The shell provides commands, such as top, that you can use to see what the heck is going on with all your computer s running processes. After all, it s your computer, and you have a right to know. This kind of information can be especially useful if your Mac seems to have slowed to a crawl, and you suspect an application is taking more than its share of your CPU time. To use top, just type top at the shell prompt. You see (Figure 10-1) a constantly updating table that gives various information about each running process, including its name, process ID number (PID), and the percentage of the CPU s time that s currently devoted to it.

= m.

(3) X (m x n), A (m x n) :

You enter an array formula by pressing Ctrl+Shift+Enter. To remind you that a formula is an array formula, Excel surrounds it with brackets ({ }) in the formula bar. Don t enter the brackets yourself.

Functions of the First Four OSI Layers. Only the rst four OSI layers are described in the following paragraphs. Layers 5, 6, and 7 are more in the realm of software design. Physical layer. The physical layer is layer 1, the lowest OSI layer. It provides the physical connectivity between two data terminals who wish to communicate. The services it provides to the data link layer (layer 2) are those required to connect, maintain the connection, and disconnect the physical circuits that form the physical connectivity. The physical layer represents the traditional interface between the DCE and DTE, described in Section 10.8.

where the inequality follows from the fact that conditioning reduces differential entropy, and the nal equality from the fact that X1 and X2 are independent. Substituting the expressions for the differential entropies of a normal random variable, we obtain 1 1 log(2 e)n |A| > log(2 e)n |B|, 2 2 which is equivalent to the desired lemma. Lemma 9.6.3 For two n-dimensional random vectors X and Z, |KX+Z | 2n |KX + KZ |. Proof: From Lemma 9.6.1, 2(KX + KZ ) KX+Z = KX Z 0, (9.131) (9.130) (9.129)

To communicate between movies on different levels, you must use the level name in the target path. For example, the portland instance would address the atlanta instance as follows:

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of complex systems and are widely used in physics and various domains of biology, including neuroscience (Amit, 1989) and population dynamics (May, 1981) A set of elements is speci ed to represent the basic units (eg, neurons, people) in the process under consideration Each element can adopt a nite number of discrete states (eg, activated vs inhibited, pro- vs antiabortion) The elements are arranged in a spatial con guration, the most common of which is a twodimensional grid The state of an element at t + 1 depends on the states of the neighboring elements at time t The exact form of this dependence is speci ed by so-called updating rules The dynamics of cellular automata depend on the nature of the updating rule and on the format of the grid dictating the neighborhood structure.

is done through the SV1 power switch. Move it to position 2-3 (toward the USB port) to enable USB power and move it to position 1-2 (toward the external power input) to enable external power. Figure 13-23 shows the SV1 switch in the external power position.

Two classes of cellular automata models are used to characterize social processes In both, elements represent individuals in a social system In one, personal characteristics change as a result of updating rules This approach explores changes in attitudes and opinions that occur as a result of social interaction In the other class, individuals maintain stable characteristics but may change their physical location This approach has revealed the emergence of spatial patterns on the basis of stable values and preferences Shelling (1969, 1971), for instance, developed an updating rule specifying that an individual who has more dissimilar than similar neighbors will move to a different random location Simulations based on this simple rule demonstrated the emergence of spatial patterns corresponding to social segregation Both classes of models reveal the emergence of regularities and patterns on a global level that were not directly programmed into the individual elements.