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Assuming that the avahi package was installed, the command to remove the package failed because multiple packages of the same base name were installed. The second command succeeded (although it did so silently). Notice, however, that the last command failed because of dependency issues. The best way to resolve dependency issues is to use yum or work through the dependencies by hand. However, you may reach a point where you have to force the install or removal of a package. WARNING! Doing this is DANGEROUS and may result in an unstable system. Make sure you know precisely what you re doing. Here, you specify that you want to remove the i386 version of the package, and ignore dependencies:

3. Double-click the layer in the Time Layout window to display the Layer window. In the Layer pop-up window, choose Anchor Point Path. Click the circle and move it to where you want the path to begin. 4. Now, move the Time Marker to a new position in the Timeline and then move the circle again to start creating the motion path. Continue moving the Time Marker, then move the circle to a new position until you ve finished creating your motion path. To edit the motion path, you can move the Layer keyframe, Direction handles, or Direction lines. 5. Depending upon the path you ve created, you may want to use the Layer Transform Auto-Orient Rotation command. The Auto-Orient Rotation command allows you to rotate an object along a path, so that the object is facing a different direction. 6. Now it s time to preview the motion you just created. You can either use the Composition Preview Motion with Trails command, the Composition Preview Wireframe Preview command, or the Composition Preview RAM Preview command. (The Motion with Trails is the fastest, and the RAM Preview is the slowest.) 7. If you want to keep your work, don t forget to save it. The easy way of animating in After Effects is to use the Sketch a Motion option. This option enables you to draw your path freehand. Here s how to animate using the After Effects s Sketch a Motion option:

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NEURONS FOR COMBINATIONAL LEARNING Neurons for basic survival have evolved over millions of years, and fundamental neural structures apparently are present at birth. Beyond this, all else is learned over a lifetime. What follows is a model of the result of learning. Learning to detect a new feature such as the color chartreuse, a mixture of yellow and green, can be modeled as follows. Assume the existence of a neuron in the encoder block that is dedicated to green and another that is dedicated to yellow. At rst a person may recall words involving green and then words involving yellow into short-term memory and then alternate these words with a de nition of chartreuse to identify chartreuse. But going through short-term memory this way is inef cient. A person who needs to identify chartreuse eventually learns to recognize it automatically, without having to ponder the components of green and yellow and tie up short-term memory. In order to learn chartreuse, a new unused neuron must develop near those two neurons in the encoder block dedicated to yellow and green. As chartreuse is brought repeatedly into short-term memory, a need-to-learn signal is made to occur. This signal and the cues for yellow and green are able to pinpoint those locations on the yellow and green neurons where synapses are needed. With the aid of interneurons for learning, described below, the yellow and green neurons develop synapses to the new neuron, as

implemented in home and of ce environments whereas roads, railways and forests may be some examples of outdoor environments for CO applications Most of the factors listed in this section, such as localization, security and mobility, introduce more challenging requirements for outdoor applications The other properties, given below, are the general system requirements of CO applications CO applications require some of these requirements, depending on the task on which they are focused Different applications require different levels of importance of the below properties Automation: nodes can be remotely controlled or fully unattended and autonomous In the applications of the latter class, nodes make autonomous decisions according to the collected information Particularly, applications operating without human intervention and including robots and automated machinery require a high degree of autonomy.

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